Prediction of type 1 diabetes using a genetic risk model in the Diabetes Autoimmunity Study in the Young
Background Genetic predisposition for type 1 diabetes (T1D) is largely determined by human leukocyte antigen (HLA) genes; however, over 50 other genetic regions confer susceptibility. We evaluated a previously reported 10‐factor weighted model derived from the Type 1 Diabetes Genetics Consortium to...
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| Published in | Pediatric diabetes Vol. 19; no. 2; pp. 277 - 283 |
|---|---|
| Main Authors | , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Former Munksgaard
John Wiley & Sons A/S
01.03.2018
John Wiley & Sons, Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1399-543X 1399-5448 1399-5448 |
| DOI | 10.1111/pedi.12543 |
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| Abstract | Background
Genetic predisposition for type 1 diabetes (T1D) is largely determined by human leukocyte antigen (HLA) genes; however, over 50 other genetic regions confer susceptibility. We evaluated a previously reported 10‐factor weighted model derived from the Type 1 Diabetes Genetics Consortium to predict the development of diabetes in the Diabetes Autoimmunity Study in the Young (DAISY) prospective cohort. Performance of the model, derived from individuals with first‐degree relatives (FDR) with T1D, was evaluated in DAISY general population (GP) participants as well as FDR subjects.
Methods
The 10‐factor weighted risk model (HLA,
PTPN22
,
INS
,
IL2RA
,
ERBB3
,
ORMDL3
,
BACH2
,
IL27
,
GLIS3
,
RNLS
), 3‐factor model (HLA,
PTPN22, INS
), and HLA alone were compared for the prediction of diabetes in children with complete SNP data (n = 1941).
Results
Stratification by risk score significantly predicted progression to diabetes by Kaplan‐Meier analysis (GP: P = .00006; FDR: P = .0022). The 10‐factor model performed better in discriminating diabetes outcome than HLA alone (GP, P = .03; FDR, P = .01). In GP, the restricted 3‐factor model was superior to HLA (P = .03), but not different from the 10‐factor model (P = .22). In contrast, for FDR the 3‐factor model did not show improvement over HLA (P = .12) and performed worse than the 10‐factor model (P = .02)
Conclusions
We have shown a 10‐factor risk model predicts development of diabetes in both GP and FDR children. While this model was superior to a minimal model in FDR, it did not confer improvement in GP.
Differences in model performance in FDR vs GP children may lead to important insights into screening strategies specific to these groups. |
|---|---|
| AbstractList | Background
Genetic predisposition for type 1 diabetes (T1D) is largely determined by human leukocyte antigen (HLA) genes; however, over 50 other genetic regions confer susceptibility. We evaluated a previously reported 10‐factor weighted model derived from the Type 1 Diabetes Genetics Consortium to predict the development of diabetes in the Diabetes Autoimmunity Study in the Young (DAISY) prospective cohort. Performance of the model, derived from individuals with first‐degree relatives (FDR) with T1D, was evaluated in DAISY general population (GP) participants as well as FDR subjects.
Methods
The 10‐factor weighted risk model (HLA,
PTPN22
,
INS
,
IL2RA
,
ERBB3
,
ORMDL3
,
BACH2
,
IL27
,
GLIS3
,
RNLS
), 3‐factor model (HLA,
PTPN22, INS
), and HLA alone were compared for the prediction of diabetes in children with complete SNP data (n = 1941).
Results
Stratification by risk score significantly predicted progression to diabetes by Kaplan‐Meier analysis (GP: P = .00006; FDR: P = .0022). The 10‐factor model performed better in discriminating diabetes outcome than HLA alone (GP, P = .03; FDR, P = .01). In GP, the restricted 3‐factor model was superior to HLA (P = .03), but not different from the 10‐factor model (P = .22). In contrast, for FDR the 3‐factor model did not show improvement over HLA (P = .12) and performed worse than the 10‐factor model (P = .02)
Conclusions
We have shown a 10‐factor risk model predicts development of diabetes in both GP and FDR children. While this model was superior to a minimal model in FDR, it did not confer improvement in GP.
Differences in model performance in FDR vs GP children may lead to important insights into screening strategies specific to these groups. Background Genetic predisposition for type 1 diabetes (T1D) is largely determined by human leukocyte antigen (HLA) genes; however, over 50 other genetic regions confer susceptibility. We evaluated a previously reported 10-factor weighted model derived from the Type 1 Diabetes Genetics Consortium to predict the development of diabetes in the Diabetes Autoimmunity Study in the Young (DAISY) prospective cohort. Performance of the model, derived from individuals with first-degree relatives (FDR) with T1D, was evaluated in DAISY general population (GP) participants as well as FDR subjects. Methods The 10-factor weighted risk model (HLA, PTPN22, INS, IL2RA, ERBB3, ORMDL3, BACH2, IL27, GLIS3, RNLS), 3-factor model (HLA, PTPN22, INS), and HLA alone were compared for the prediction of diabetes in children with complete SNP data (n = 1941). Results Stratification by risk score significantly predicted progression to diabetes by Kaplan-Meier analysis (GP: P = .00006; FDR: P = .0022). The 10-factor model performed better in discriminating diabetes outcome than HLA alone (GP, P = .03; FDR, P = .01). In GP, the restricted 3-factor model was superior to HLA (P = .03), but not different from the 10-factor model (P = .22). In contrast, for FDR the 3-factor model did not show improvement over HLA (P = .12) and performed worse than the 10-factor model (P = .02) Conclusions We have shown a 10-factor risk model predicts development of diabetes in both GP and FDR children. While this model was superior to a minimal model in FDR, it did not confer improvement in GP. Differences in model performance in FDR vs GP children may lead to important insights into screening strategies specific to these groups. Genetic predisposition for type 1 diabetes (T1D) is largely determined by human leukocyte antigen (HLA) genes; however, over 50 other genetic regions confer susceptibility. We evaluated a previously reported 10-factor weighted model derived from the Type 1 Diabetes Genetics Consortium to predict the development of diabetes in the Diabetes Autoimmunity Study in the Young (DAISY) prospective cohort. Performance of the model, derived from individuals with first-degree relatives (FDR) with T1D, was evaluated in DAISY general population (GP) participants as well as FDR subjects. The 10-factor weighted risk model (HLA, PTPN22 , INS , IL2RA , ERBB3 , ORMDL3 , BACH2 , IL27 , GLIS3 , RNLS ), 3-factor model (HLA, PTPN22, INS ), and HLA alone were compared for the prediction of diabetes in children with complete SNP data (n = 1941). Stratification by risk score significantly predicted progression to diabetes by Kaplan-Meier analysis (GP: P = .00006; FDR: P = .0022). The 10-factor model performed better in discriminating diabetes outcome than HLA alone (GP, P = .03; FDR, P = .01). In GP, the restricted 3-factor model was superior to HLA (P = .03), but not different from the 10-factor model (P = .22). In contrast, for FDR the 3-factor model did not show improvement over HLA (P = .12) and performed worse than the 10-factor model (P = .02) CONCLUSIONS: We have shown a 10-factor risk model predicts development of diabetes in both GP and FDR children. While this model was superior to a minimal model in FDR, it did not confer improvement in GP. Differences in model performance in FDR vs GP children may lead to important insights into screening strategies specific to these groups. Genetic predisposition for type 1 diabetes (T1D) is largely determined by human leukocyte antigen (HLA) genes; however, over 50 other genetic regions confer susceptibility. We evaluated a previously reported 10-factor weighted model derived from the Type 1 Diabetes Genetics Consortium to predict the development of diabetes in the Diabetes Autoimmunity Study in the Young (DAISY) prospective cohort. Performance of the model, derived from individuals with first-degree relatives (FDR) with T1D, was evaluated in DAISY general population (GP) participants as well as FDR subjects.BACKGROUNDGenetic predisposition for type 1 diabetes (T1D) is largely determined by human leukocyte antigen (HLA) genes; however, over 50 other genetic regions confer susceptibility. We evaluated a previously reported 10-factor weighted model derived from the Type 1 Diabetes Genetics Consortium to predict the development of diabetes in the Diabetes Autoimmunity Study in the Young (DAISY) prospective cohort. Performance of the model, derived from individuals with first-degree relatives (FDR) with T1D, was evaluated in DAISY general population (GP) participants as well as FDR subjects.The 10-factor weighted risk model (HLA, PTPN22 , INS , IL2RA , ERBB3 , ORMDL3 , BACH2 , IL27 , GLIS3 , RNLS ), 3-factor model (HLA, PTPN22, INS ), and HLA alone were compared for the prediction of diabetes in children with complete SNP data (n = 1941).METHODSThe 10-factor weighted risk model (HLA, PTPN22 , INS , IL2RA , ERBB3 , ORMDL3 , BACH2 , IL27 , GLIS3 , RNLS ), 3-factor model (HLA, PTPN22, INS ), and HLA alone were compared for the prediction of diabetes in children with complete SNP data (n = 1941).Stratification by risk score significantly predicted progression to diabetes by Kaplan-Meier analysis (GP: P = .00006; FDR: P = .0022). The 10-factor model performed better in discriminating diabetes outcome than HLA alone (GP, P = .03; FDR, P = .01). In GP, the restricted 3-factor model was superior to HLA (P = .03), but not different from the 10-factor model (P = .22). In contrast, for FDR the 3-factor model did not show improvement over HLA (P = .12) and performed worse than the 10-factor model (P = .02) CONCLUSIONS: We have shown a 10-factor risk model predicts development of diabetes in both GP and FDR children. While this model was superior to a minimal model in FDR, it did not confer improvement in GP. Differences in model performance in FDR vs GP children may lead to important insights into screening strategies specific to these groups.RESULTSStratification by risk score significantly predicted progression to diabetes by Kaplan-Meier analysis (GP: P = .00006; FDR: P = .0022). The 10-factor model performed better in discriminating diabetes outcome than HLA alone (GP, P = .03; FDR, P = .01). In GP, the restricted 3-factor model was superior to HLA (P = .03), but not different from the 10-factor model (P = .22). In contrast, for FDR the 3-factor model did not show improvement over HLA (P = .12) and performed worse than the 10-factor model (P = .02) CONCLUSIONS: We have shown a 10-factor risk model predicts development of diabetes in both GP and FDR children. While this model was superior to a minimal model in FDR, it did not confer improvement in GP. Differences in model performance in FDR vs GP children may lead to important insights into screening strategies specific to these groups. |
| Author | Krumsiek, Jan Norris, Jill M Rewers, Marian J Laimighofer, Michael Theis, Fabian J Ziegler, Anette‐Gabriele Frohnert, Brigitte I Winkler, Christiane Steck, Andrea K |
| AuthorAffiliation | 2 Institute of Computational Biology, Helmholtz Zentrum München, München-Neuherberg 85764 Germany 4 Institute of Diabetes Research, Helmholtz Zentrum München and Forschergruppe Diabetes, Klinikum rechts der Isar, Technische Universität München, Neuherberg 85764 Germany 5 Department of Epidemiology, Colorado School of Public Health, University of Colorado, Aurora, CO, 80045 USA 3 German Center for Diabetes Research (DZD), München-Neuherberg 85764 Germany 1 Barbara Davis Center for Childhood Diabetes, School of Medicine, University of Colorado, Aurora, CO 80045 USA |
| AuthorAffiliation_xml | – name: 2 Institute of Computational Biology, Helmholtz Zentrum München, München-Neuherberg 85764 Germany – name: 3 German Center for Diabetes Research (DZD), München-Neuherberg 85764 Germany – name: 4 Institute of Diabetes Research, Helmholtz Zentrum München and Forschergruppe Diabetes, Klinikum rechts der Isar, Technische Universität München, Neuherberg 85764 Germany – name: 1 Barbara Davis Center for Childhood Diabetes, School of Medicine, University of Colorado, Aurora, CO 80045 USA – name: 5 Department of Epidemiology, Colorado School of Public Health, University of Colorado, Aurora, CO, 80045 USA |
| Author_xml | – sequence: 1 givenname: Brigitte I orcidid: 0000-0002-6636-4048 surname: Frohnert fullname: Frohnert, Brigitte I email: brigitte.frohnert@ucdenver.edu organization: University of Colorado – sequence: 2 givenname: Michael surname: Laimighofer fullname: Laimighofer, Michael organization: Helmholtz Zentrum München – sequence: 3 givenname: Jan surname: Krumsiek fullname: Krumsiek, Jan organization: German Center for Diabetes Research (DZD) – sequence: 4 givenname: Fabian J surname: Theis fullname: Theis, Fabian J organization: Helmholtz Zentrum München – sequence: 5 givenname: Christiane surname: Winkler fullname: Winkler, Christiane organization: Universität München – sequence: 6 givenname: Jill M surname: Norris fullname: Norris, Jill M organization: University of Colorado – sequence: 7 givenname: Anette‐Gabriele surname: Ziegler fullname: Ziegler, Anette‐Gabriele organization: Universität München – sequence: 8 givenname: Marian J surname: Rewers fullname: Rewers, Marian J organization: University of Colorado – sequence: 9 givenname: Andrea K surname: Steck fullname: Steck, Andrea K organization: University of Colorado |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28695611$$D View this record in MEDLINE/PubMed |
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| Keywords | epidemiology risk factors diabetes mellitus type 1 prospective study child |
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Genetic predisposition for type 1 diabetes (T1D) is largely determined by human leukocyte antigen (HLA) genes; however, over 50 other genetic... Genetic predisposition for type 1 diabetes (T1D) is largely determined by human leukocyte antigen (HLA) genes; however, over 50 other genetic regions confer... Background Genetic predisposition for type 1 diabetes (T1D) is largely determined by human leukocyte antigen (HLA) genes; however, over 50 other genetic... |
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| SubjectTerms | Autoantibodies - analysis Autoimmunity Child Child, Preschool Children Cohort Studies Diabetes Diabetes mellitus Diabetes mellitus (insulin dependent) Diabetes Mellitus, Type 1 - blood Diabetes Mellitus, Type 1 - genetics Diabetes Mellitus, Type 1 - immunology Discriminant Analysis Disease-Free Survival epidemiology ErbB-3 protein Family Health Female Genetic Predisposition to Disease Histocompatibility antigen HLA HLA-D Antigens - chemistry HLA-D Antigens - genetics Humans Infant Insulin - chemistry Insulin - genetics Interleukin 2 receptors Interleukin 27 Kaplan-Meier Estimate Longitudinal Studies Male Models, Genetic Polymorphism, Single Nucleotide Prospective Studies prospective study Protein Tyrosine Phosphatase, Non-Receptor Type 22 - chemistry Protein Tyrosine Phosphatase, Non-Receptor Type 22 - genetics Protein-tyrosine-phosphatase risk factors Single-nucleotide polymorphism type 1 |
| Title | Prediction of type 1 diabetes using a genetic risk model in the Diabetes Autoimmunity Study in the Young |
| URI | https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fpedi.12543 https://www.ncbi.nlm.nih.gov/pubmed/28695611 https://www.proquest.com/docview/2001649131 https://www.proquest.com/docview/1917959883 https://pubmed.ncbi.nlm.nih.gov/PMC5764829 |
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