BACKGROUND: Type 1 diabetes (T1D) is a clinically heterogeneous disease. The presence of associated autoimmune diseases (AAIDs) may represent a distinct form of autoimmune diabetes, with involvement of specific mechanisms. The aim of this study was to find predictors of AAIDs in the Type 1 Diabetes Genetics Consortium data set. METHODS: Three thousand two hundred and sixty-three families with at least two siblings with T1D were included. Clinical information was obtained using questionnaires, anti-GAD (glutamic acid decarboxylase) and anti-protein tyrosine phosphatase (IA-2) were measured and human leukocyte antigen (HLA) genotyping was performed. Siblings with T1D with and without AAIDs were compared and a multivariate regression analysis was performed to find predictors of AAIDs. T1D-associated HLA haplotypes were defined as the four most susceptible and protective, respectively. RESULTS: One or more AAIDs were present in 14.4% of the T1D affected siblings. Age of diabetes onset, current age and time since diagnosis were higher, there was a female predominance and more family history of AAIDs in the group with AAIDs, as well as more frequent anti-GAD and less frequent anti-IA-2 antibodies. Risk and protective HLA haplotype distributions were similar, though DRB1*0301-DQA1*0501-DQB1*0201 was more frequent in the group with AAIDs. In the multivariate analysis, female gender, age of onset, family history of AAID, time since diagnosis and anti-GAD positivity were significantly associated with AAIDs. CONCLUSIONS: In patients with T1D, the presence of AAIDs is associated with female predominance, more frequent family history of AAIDs, later onset of T1D and more anti-GAD antibodies, despite longer duration of the disease. The predominance of certain HLA haplotypes suggests that specific mechanisms of disease may be involved.
BACKGROUND:Type 1 diabetes (T1D) is a clinically heterogeneous disease. The presence of associated autoimmune diseases (AAIDs) may represent a distinct form of autoimmune diabetes, with involvement of specific mechanisms. The aim of this study was to find predictors of AAIDs in the Type 1 Diabetes Genetics Consortium data set. METHODS: Three thousand two hundred and sixty-three families with at least two siblings with T1D were included. Clinical information was obtained using questionnaires, anti-GAD (glutamic acid decarboxylase) and anti-protein tyrosine phosphatase (IA-2) were measured and human leukocyte antigen (HLA) genotyping was performed. Siblings with T1D with and without AAIDs were compared and a multivariate regression analysis was performed to find predictors of AAIDs. T1D-associated HLA haplotypes were defined as the four most susceptible and protective, respectively. RESULTS: One or more AAIDs were present in 14.4% of the T1D affected siblings. Age of diabetes onset, current age and time since diagnosis were higher, there was a female predominance and more family history of AAIDs in the group with AAIDs, as well as more frequent anti-GAD and less frequent anti-IA-2 antibodies. Risk and protective HLA haplotype distributions were similar, though DRB1*0301-DQA1*0501-DQB1*0201 was more frequent in the group with AAIDs. In the multivariate analysis, female gender, age of onset, family history of AAID, time since diagnosis and anti-GAD positivity were significantly associated with AAIDs. CONCLUSIONS: In patients with T1D, the presence of AAIDs is associated with female predominance, more frequent family history of AAIDs, later onset of T1D and more anti-GAD antibodies, despite longer duration of the disease. The predominance of certain HLA haplotypes suggests that specific mechanisms of disease may be involved.
Authors: N G Morgenthaler; J Seissler; P Achenbach; D Glawe; M Payton; H M Meinck; M R Christie; W A Scherbaum Journal: Autoimmunity Date: 1997 Impact factor: 2.815
Authors: M Pietropaolo; M Peakman; S L Pietropaolo; M M Zanone; T P Foley; D J Becker; M Trucco Journal: J Autoimmun Date: 1998-02 Impact factor: 7.094
Authors: Polly J Bingley; Alistair J K Williams; Peter G Colman; Shane A Gellert; George Eisenbarth; Liping Yu; Letitia H Perdue; June J Pierce; Joan E Hilner; Concepcion Nierras; Beena Akolkar; Michael W Steffes Journal: Clin Trials Date: 2010 Impact factor: 2.486
Authors: J Ramón Bilbao; Ainhoa Martín-Pagola; Guiomar Pérez De Nanclares; Begoña Calvo; Juan Carlos Vitoria; Federico Vázquez; Luis Castaño Journal: Ann N Y Acad Sci Date: 2003-11 Impact factor: 5.691
Authors: R Wagner; J M McNally; E Bonifacio; S Genovese; A Foulis; M McGill; M R Christie; C Betterle; E Bosi; G F Bottazzo Journal: Diabetes Date: 1994-07 Impact factor: 9.461
Authors: Jing W Hughes; Yicheng K Bao; Maamoun Salam; Prajesh Joshi; C Rachel Kilpatrick; Kavita Juneja; David Nieves; Victoria Bouhairie; Olivia J Jordan; Erica C Blustein; Garry S Tobin; Janet B McGill Journal: Diabetes Care Date: 2018-10-25 Impact factor: 19.112
Authors: Anna Parkkola; Antti-Pekka Laine; Markku Karhunen; Taina Härkönen; Samppa J Ryhänen; Jorma Ilonen; Mikael Knip Journal: PLoS One Date: 2017-11-28 Impact factor: 3.240