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

CONTEXT: Some individuals represent forms of "atypical diabetes" (AD) that 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 atypical diabetes frequency of 11.5% in the CCHC (Study A) and 5.3% in the pediatric TCHRNO-1 (Study B). Cluster analysis identified four 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 T1D pediatric patients in Study B revealed two 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.