PuSH - Publikationsserver des Helmholtz Zentrums München

Frohnert, B.I.* ; Laimighofer, M. ; Krumsiek, J. ; Theis, F.J. ; Winkler, C. ; Norris, J.M.* ; Ziegler, A.-G. ; Rewers, M.J.* ; Steck, A.K.*

Prediction of type 1 diabetes using a genetic risk model in the Diabetes Autoimmunity Study in the Young.

Pediatr. Diabetes 19, 277-283 (2018)
DOI PMC
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
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.
Impact Factor
Scopus SNIP
Web of Science
Times Cited
Scopus
Cited By
Altmetric
3.161
1.834
12
13
Tags
Anmerkungen
Besondere Publikation
Auf Hompepage verbergern

Zusatzinfos bearbeiten
Eigene Tags bearbeiten
Privat
Eigene Anmerkung bearbeiten
Privat
Auf Publikationslisten für
Homepage nicht anzeigen
Als besondere Publikation
markieren
Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Child ; Diabetes Mellitus ; Epidemiology ; Prospective Study ; Risk Factors ; Type 1; Genome-wide Association; Multiplex Families; Susceptibility Genes; Hla Markers; Disease; Loci; Autoantibodies; Metaanalysis; Childhood; Linkage
Sprache englisch
Veröffentlichungsjahr 2018
Prepublished im Jahr 2017
HGF-Berichtsjahr 2017
ISSN (print) / ISBN 1399-543X
e-ISSN 1399-5448
Zeitschrift Pediatric Diabetes
Quellenangaben Band: 19, Heft: 2, Seiten: 277-283 Artikelnummer: , Supplement: ,
Verlag Wiley
Verlagsort Hoboken
Begutachtungsstatus Peer reviewed
POF Topic(s) 30205 - Bioengineering and Digital Health
30201 - Metabolic Health
Forschungsfeld(er) Enabling and Novel Technologies
Helmholtz Diabetes Center
PSP-Element(e) G-554100-001
G-503800-001
G-502100-001
Scopus ID 85022344673
PubMed ID 28695611
Erfassungsdatum 2017-07-31