Abstract
AIM: The region on chromosome 6p21 (IDDM1) confers the largest part of genetic susceptibility to type 1 diabetes (T1D) with particular human leucocyte antigen (HLA) alleles predisposing and others protecting from it. As T1D is primarily a "sporadic" disease, the pathophysiology must involve gene-environment interactions. We searched for indirect evidence for such major histocompatibility complex (MHC)-environment interactions by asking two questions: (i) can the degree of an HLA association vary over time periods? and (ii) if a prenatal event like an intrauterine infection - that might cluster in seasons - leads to differences of HLA associations in patients with particular birth months? METHODS: We screened the Type 1 Diabetes Genetics Consortium (T1DGC) database (in addition our own database and the original UK, US and SCAND databases) for MHC DR-DQ and CTLA4 associations. First, we separated the groups of patients with onset of disease before 1980 in comparison with onset after 1980. Second, we analysed the data according to dates of birth (grouped in months). Not all patients' dates of birth or manifestation periods were available, leading to different group sizes. There were 282 patients analysed for manifestation periods and 329 for birth month. RESULTS: The cohorts of manifestation before 1980 demonstrated a significantly lower frequency of DQ2/X (2 vs. 14.2%; p = 0.03). There was a trend for DQ8/x to be more frequent for manifestations before 1980 (34 vs. 21.6%; p < 0.10). Other alleles did not differ significantly. The months of birth were not evenly distributed. Significant deviations from the whole group were seen in August (DQ2/8 trough and DQx/x high), whereas birth in September was more frequent in DQ8/x or DQ8/8 carriers. This pattern was significantly different from the expected distribution of months at birth (13.9 vs. 7.6%; p < 0.04). CONCLUSIONS: We demonstrate the feasibility of an analysis that searches for indirect evidence of gene-environment interactions. These preliminary data need to be confirmed in larger data sets.