Data Mining Framework for Discovering and Clustering Phenotypes of Atypical Diabetes.

CONTEXT: Some individuals present with forms of diabetes that are "atypical" (AD), which do not conform to typical features of either type 1 diabetes (T1D) or type 2 diabetes (T2D). These forms of AD display a range of phenotypic characteristics that likely reflect different endotypes based on unique etiologies or pathogenic processes. OBJECTIVE: To develop an analytical approach to identify and cluster phenotypes of AD. METHODS: We developed Discover Atypical Diabetes (DiscoverAD), a data mining framework, to identify and cluster phenotypes of AD. DiscoverAD was trained against characteristics of manually classified patients with AD among 278 adults with diabetes within the Cameron County Hispanic Cohort (CCHC) (Study A). We then tested DiscoverAD in a separate population of 758 multiethnic children with T1D within the Texas Children's Hospital Registry for New-Onset Type 1 Diabetes (TCHRNO-1) (Study B). RESULTS: We identified an AD frequency of 11.5% in the CCHC (Study A) and 5.3% in the pediatric TCHRNO-1 (Study B). Cluster analysis identified 4 distinct groups of AD in Study A: cluster 1, positive for the 65 kDa glutamate decarboxylase autoantibody (GAD65Ab), adult-onset, long disease duration, preserved beta-cell function, no insulin treatment; cluster 2, GAD65Ab negative, diagnosed at age ≤21 years; cluster 3, GAD65Ab negative, adult-onset, poor beta-cell function, lacking central obesity; cluster 4, diabetic ketoacidosis (DKA)-prone participants lacking a typical T1D phenotype. Applying DiscoverAD to the pediatric patients with T1D in Study B revealed 2 distinct groups of AD: cluster 1, autoantibody negative, poor beta-cell function, lower body mass index (BMI); cluster 2, autoantibody positive, higher BMI, higher incidence of DKA. CONCLUSION: DiscoverAD can be adapted to different datasets to identify and define phenotypes of participants with AD based on available clinical variables.

Telomere length is not a main factor for the development of islet autoimmunity and type 1 diabetes in the TEDDY study.

The Environmental Determinants of Diabetes in the Young (TEDDY) study enrolled 8676 children, 3-4 months of age, born with HLA-susceptibility genotypes for islet autoimmunity (IA) and type 1 diabetes (T1D). Whole-genome sequencing (WGS) was performed in 1119 children in a nested case-control study design. Telomere length was estimated from WGS data using five tools: Computel, Telseq, Telomerecat, qMotif and Motif_counter. The estimated median telomere length was 5.10 kb (IQR 4.52-5.68 kb) using Computel. The age when the blood sample was drawn had a significant negative correlation with telomere length (P = 0.003). European children, particularly those from Finland (P = 0.041) and from Sweden (P = 0.001), had shorter telomeres than children from the U.S.A. Paternal age (P = 0.019) was positively associated with telomere length. First-degree relative status, presence of gestational diabetes in the mother, and maternal age did not have a significant impact on estimated telomere length. HLA-DR4/4 or HLA-DR4/X children had significantly longer telomeres compared to children with HLA-DR3/3 or HLA-DR3/9 haplogenotypes (P = 0.008). Estimated telomere length was not significantly different with respect to any IA (P = 0.377), IAA-first (P = 0.248), GADA-first (P = 0.248) or T1D (P = 0.861). These results suggest that telomere length has no major impact on the risk for IA, the first step to develop T1D. Nevertheless, telomere length was shorter in the T1D high prevalence populations, Finland and Sweden.