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Joint modeling of longitudinal autoantibody patterns and progression to type 1 diabetes : results from the TEDDY study

Köhler, Meike; Beyerlein, Andreas; Vehik, Kendra LU ; Greven, Sonja; Umlauf, Nikolaus; Lernmark, Åke LU ; Hagopian, William A.; Rewers, Marian; She, Jin-Xiong and Toppari, Jorma, et al. (2017) In Acta Diabetologica 54(11). p.1009-1017
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... (More)

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.

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publication status
published
subject
keywords
Journal Article
in
Acta Diabetologica
volume
54
issue
11
pages
1009 - 1017
publisher
Springer
external identifiers
  • scopus:85028622724
  • wos:000413142300004
ISSN
1432-5233
DOI
10.1007/s00592-017-1033-7
language
English
LU publication?
yes
id
d069a342-35b5-4fb4-a799-52f30fa96f2e
date added to LUP
2017-09-21 11:13:50
date last changed
2018-03-11 04:43:31
@article{d069a342-35b5-4fb4-a799-52f30fa96f2e,
  abstract     = {<p>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.</p><p>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.</p><p>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.</p><p>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.</p>},
  author       = {Köhler, Meike and Beyerlein, Andreas and Vehik, Kendra and Greven, Sonja and Umlauf, Nikolaus and Lernmark, Åke and Hagopian, William A. and Rewers, Marian and She, Jin-Xiong and Toppari, Jorma and Akolkar, Beena and Krischer, Jeffrey P. and Bonifacio, Ezio and Ziegler, Anette-G and , },
  issn         = {1432-5233},
  keyword      = {Journal Article},
  language     = {eng},
  month        = {08},
  number       = {11},
  pages        = {1009--1017},
  publisher    = {Springer},
  series       = {Acta Diabetologica},
  title        = {Joint modeling of longitudinal autoantibody patterns and progression to type 1 diabetes : results from the TEDDY study},
  url          = {http://dx.doi.org/10.1007/s00592-017-1033-7},
  volume       = {54},
  year         = {2017},
}