PMID- 28695611
OWN - NLM
STAT- MEDLINE
DCOM- 20181102
LR  - 20210109
IS  - 1399-5448 (Electronic)
IS  - 1399-543X (Print)
IS  - 1399-543X (Linking)
VI  - 19
IP  - 2
DP  - 2018 Mar
TI  - Prediction of type 1 diabetes using a genetic risk model in the Diabetes 
      Autoimmunity Study in the Young.
PG  - 277-283
LID - 10.1111/pedi.12543 [doi]
AB  - 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.
CI  - (c) 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
FAU - Frohnert, Brigitte I
AU  - Frohnert BI
AUID- ORCID: 0000-0002-6636-4048
AD  - Barbara Davis Center for Childhood Diabetes, School of Medicine, University of 
      Colorado, Aurora, Colorado.
FAU - Laimighofer, Michael
AU  - Laimighofer M
AD  - Institute of Computational Biology, Helmholtz Zentrum Munchen, 
      Munchen-Neuherberg, Germany.
FAU - Krumsiek, Jan
AU  - Krumsiek J
AD  - Institute of Computational Biology, Helmholtz Zentrum Munchen, 
      Munchen-Neuherberg, Germany.
AD  - German Center for Diabetes Research (DZD), Munchen-Neuherberg, Germany.
FAU - Theis, Fabian J
AU  - Theis FJ
AD  - Institute of Computational Biology, Helmholtz Zentrum Munchen, 
      Munchen-Neuherberg, Germany.
FAU - Winkler, Christiane
AU  - Winkler C
AD  - Institute of Diabetes Research, Helmholtz Zentrum Munchen and Forschergruppe 
      Diabetes, Klinikum rechts der Isar, Technische, Universitat Munchen, Neuherberg, 
      Germany.
FAU - Norris, Jill M
AU  - Norris JM
AD  - Department of Epidemiology, Colorado School of Public Health, University of 
      Colorado, Aurora, Colorado.
FAU - Ziegler, Anette-Gabriele
AU  - Ziegler AG
AD  - Institute of Diabetes Research, Helmholtz Zentrum Munchen and Forschergruppe 
      Diabetes, Klinikum rechts der Isar, Technische, Universitat Munchen, Neuherberg, 
      Germany.
FAU - Rewers, Marian J
AU  - Rewers MJ
AD  - Barbara Davis Center for Childhood Diabetes, School of Medicine, University of 
      Colorado, Aurora, Colorado.
FAU - Steck, Andrea K
AU  - Steck AK
AD  - Barbara Davis Center for Childhood Diabetes, School of Medicine, University of 
      Colorado, Aurora, Colorado.
LA  - eng
GR  - P30 DK057516/DK/NIDDK NIH HHS/United States
GR  - R01 DK032083/DK/NIDDK NIH HHS/United States
GR  - R37 DK032493/DK/NIDDK NIH HHS/United States
GR  - U01 DK062418/DK/NIDDK NIH HHS/United States
GR  - R01 DK049654/DK/NIDDK NIH HHS/United States
GR  - R01 DK032493/DK/NIDDK NIH HHS/United States
GR  - K12 DK094712/DK/NIDDK NIH HHS/United States
GR  - WT_/Wellcome Trust/United Kingdom
PT  - Comparative Study
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, Non-U.S. Gov't
DEP - 20170711
PL  - Denmark
TA  - Pediatr Diabetes
JT  - Pediatric diabetes
JID - 100939345
RN  - 0 (Autoantibodies)
RN  - 0 (HLA-D Antigens)
RN  - 0 (Insulin)
RN  - EC 3.1.3.48 (PTPN22 protein, human)
RN  - EC 3.1.3.48 (Protein Tyrosine Phosphatase, Non-Receptor Type 22)
SB  - IM
MH  - Autoantibodies/analysis
MH  - *Autoimmunity
MH  - Child
MH  - Child, Preschool
MH  - Cohort Studies
MH  - Diabetes Mellitus, Type 1/blood/*genetics/immunology
MH  - Discriminant Analysis
MH  - Disease-Free Survival
MH  - Family Health
MH  - Female
MH  - *Genetic Predisposition to Disease
MH  - HLA-D Antigens/chemistry/*genetics
MH  - Humans
MH  - Infant
MH  - Insulin/chemistry/genetics
MH  - Kaplan-Meier Estimate
MH  - Longitudinal Studies
MH  - Male
MH  - *Models, Genetic
MH  - *Polymorphism, Single Nucleotide
MH  - Prospective Studies
MH  - Protein Tyrosine Phosphatase, Non-Receptor Type 22/chemistry/genetics
PMC - PMC5764829
MID - NIHMS901955
OTO - NOTNLM
OT  - child
OT  - diabetes mellitus
OT  - epidemiology
OT  - prospective study
OT  - risk factors
OT  - type 1
EDAT- 2017/07/12 06:00
MHDA- 2018/11/06 06:00
PMCR- 2019/03/01
CRDT- 2017/07/12 06:00
PHST- 2016/11/23 00:00 [received]
PHST- 2017/04/24 00:00 [revised]
PHST- 2017/04/25 00:00 [accepted]
PHST- 2017/07/12 06:00 [pubmed]
PHST- 2018/11/06 06:00 [medline]
PHST- 2017/07/12 06:00 [entrez]
PHST- 2019/03/01 00:00 [pmc-release]
AID - 10.1111/pedi.12543 [doi]
PST - ppublish
SO  - Pediatr Diabetes. 2018 Mar;19(2):277-283. doi: 10.1111/pedi.12543. Epub 2017 Jul 
      11.