The Rare and Atypical Diabetes Network (RADIANT) study: Design and early results

Authors: SISLEY, STEPHANIE - Children'S Nutrition Research Center (CNRC)

Submitted to: Diabetes Care
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/27/2023
Publication Date: 4/27/2023
Citation: The RADIANT Study Group. 2023. The Rare and Atypical Diabetes Network (RADIANT) study: Design and early results. Diabetes Care. 46(6):1265-1270. https://doi.org/10.2337/dc22-2440.
DOI: https://doi.org/10.2337/dc22-2440

Interpretive Summary: Researchers in Houston, Texas contributed to understanding more about unusual forms of diabetes. The Rare and Atypical Diabetes Network (RADIANT) analyzed participants' genes, RNA, metabolism, and doing other tests. Out of 878 people so far, 3 had mutations in known diabetes genes and 6 new mutations were found. Common clusters include lean type 2 diabetes, antibody-negative diabetes, diabetes with abnormal fat distribution, and potentially new genetic forms. The study aims to improve identification of atypical diabetes cases. Analyzing genes and molecular data can reveal new mutations and disease mechanisms.

Technical Abstract: The Rare and Atypical Diabetes Network (RADIANT) will perform a study of individuals and, if deemed informative, a study of their family members with uncharacterized forms of diabetes. The protocol includes genomic (whole-genome [WGS], RNA, and mitochondrial sequencing), phenotypic (vital signs, biometric measurements, questionnaires, and photography), metabolomics, and metabolic assessments. Among 122 with WGS results of 878 enrolled individuals, a likely pathogenic variant in a known diabetes monogenic gene was found in 3 (2.5%), and six new monogenic variants have been identified in the SMAD5, PTPMT1, INS, NFKB1, IGF1R, and PAX6 genes. Frequent phenotypic clusters are lean type 2 diabetes, autoantibody-negative and insulin-deficient diabetes, lipodystrophic diabetes, and new forms of possible monogenic or oligogenic diabetes. The analyses will lead to improved means of atypical diabetes identification. Genetic sequencing can identify new variants, and metabolomics and transcriptomics analysis can identify novelmechanisms and biomarkers for atypical disease.