Language
English
Publication Date
3-10-2023
Journal
The Journal of Clinical Endocrinology & Metabolism
DOI
10.1210/clinem/dgac632
PMID
36314086
PMCID
PMC10211492
PubMedCentral® Posted Date
10-31-2022
PubMedCentral® Full Text Version
Post-print
Abstract
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.
Keywords
Humans, Diabetes Mellitus, Type 1, Diabetes Mellitus, Type 2, Diabetic Ketoacidosis, Autoantibodies, Phenotype, atypical diabetes, clusters, type 1 diabetes, type 2 diabetes, ketosis-prone diabetes, bioinformatics
Published Open-Access
yes
Recommended Citation
Parikh, Hemang M; Remedios, Cassandra L; Hampe, Christiane S; et al., "Data Mining Framework for Discovering and Clustering Phenotypes of Atypical Diabetes" (2023). Faculty and Staff Publications. 4341.
https://digitalcommons.library.tmc.edu/baylor_docs/4341
Included in
Biomedical Informatics Commons, Endocrinology, Diabetes, and Metabolism Commons, Medical Sciences Commons