Meike Köhler1,2,3, Andreas Beyerlein1,2,3, Kendra Vehik4, Sonja Greven5, Nikolaus Umlauf6, Åke Lernmark7, William A Hagopian8, Marian Rewers9, Jin-Xiong She10, Jorma Toppari11,12, Beena Akolkar13, Jeffrey P Krischer4, Ezio Bonifacio14, Anette-G Ziegler15,16,17. 1. Institute of Diabetes Research, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany. 2. Forschergruppe Diabetes, Klinikum rechts der Isar, Technische Universität München, Neuherberg, Germany. 3. Forschergruppe Diabetes e.V., Neuherberg, Germany. 4. Health Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, FL, USA. 5. Department of Statistics, Ludwig-Maximilians-Universität München, Munich, Germany. 6. Department of Statistics, University of Innsbruck, Innsbruck, Austria. 7. Department of Clinical Sciences, Lund University/CRC, Skåne University Hospital SUS, Malmö, Sweden. 8. Pacific Northwest Diabetes Research Institute, Seattle, WA, USA. 9. Barbara Davis Center for Childhood Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO, USA. 10. Center for Biotechnology and Genomic Medicine, Medical College of Georgia, Georgia Regents University, Augusta, GA, USA. 11. Department of Physiology Institute of Biomedicine, University of Turku, Turku, Finland. 12. Department of Pediatrics, Turku University Hospital, Turku, Finland. 13. National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, USA. 14. Center for Regenerative Therapies Dresden and Paul Langerhans Institute Dresden, Technische Universität Dresden, Dresden, Germany. 15. Institute of Diabetes Research, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany. anette-g.ziegler@helmholtz-muenchen.de. 16. Forschergruppe Diabetes, Klinikum rechts der Isar, Technische Universität München, Neuherberg, Germany. anette-g.ziegler@helmholtz-muenchen.de. 17. Forschergruppe Diabetes e.V., Neuherberg, Germany. anette-g.ziegler@helmholtz-muenchen.de.
Abstract
AIMS: The onset of clinical type 1 diabetes (T1D) is preceded by the occurrence of disease-specific autoantibodies. The level of autoantibody titers is known to be associated with progression time from the first emergence of autoantibodies to the onset of clinical symptoms, but detailed analyses of this complex relationship are lacking. We aimed to fill this gap by applying advanced statistical models. METHODS: We investigated data of 613 children from the prospective TEDDY study who were persistent positive for IAA, GADA and/or IA2A autoantibodies. We used a novel approach of Bayesian joint modeling of longitudinal and survival data to assess the potentially time- and covariate-dependent association between the longitudinal autoantibody titers and progression time to T1D. RESULTS: For all autoantibodies we observed a positive association between the titers and the T1D progression risk. This association was estimated as time-constant for IA2A, but decreased over time for IAA and GADA. For example the hazard ratio [95% credibility interval] for IAA (per transformed unit) was 3.38 [2.66, 4.38] at 6 months after seroconversion, and 2.02 [1.55, 2.68] at 36 months after seroconversion. CONCLUSIONS: These findings indicate that T1D progression risk stratification based on autoantibody titers should focus on time points early after seroconversion. Joint modeling techniques allow for new insights into these associations.
AIMS: The onset of clinical type 1 diabetes (T1D) is preceded by the occurrence of disease-specific autoantibodies. The level of autoantibody titers is known to be associated with progression time from the first emergence of autoantibodies to the onset of clinical symptoms, but detailed analyses of this complex relationship are lacking. We aimed to fill this gap by applying advanced statistical models. METHODS: We investigated data of 613 children from the prospective TEDDY study who were persistent positive for IAA, GADA and/or IA2A autoantibodies. We used a novel approach of Bayesian joint modeling of longitudinal and survival data to assess the potentially time- and covariate-dependent association between the longitudinal autoantibody titers and progression time to T1D. RESULTS: For all autoantibodies we observed a positive association between the titers and the T1D progression risk. This association was estimated as time-constant for IA2A, but decreased over time for IAA and GADA. For example the hazard ratio [95% credibility interval] for IAA (per transformed unit) was 3.38 [2.66, 4.38] at 6 months after seroconversion, and 2.02 [1.55, 2.68] at 36 months after seroconversion. CONCLUSIONS: These findings indicate that T1D progression risk stratification based on autoantibody titers should focus on time points early after seroconversion. Joint modeling techniques allow for new insights into these associations.
Entities:
Keywords:
Autoantibodies; Joint modeling; Type 1 diabetes
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