Systematic Genetic Study of Youth With Diabetes in a Single Country Reveals the Prevalence of Diabetes Subtypes, Novel Candidate Genes, and Response to Precision Therapy
Author(s) -
Ingrida Stankutė,
Rasa Verkauskienė,
JeanLouis Blouin,
Philippe Klee,
Rimantė Dobrovolskienė,
Evalda Danytė,
Mirjam Dirlewanger,
Federico Santoni,
Dovilė Ražanskaitė-Virbickienė,
Dalė Marčiulionytė,
Edita Jašinskienė,
Giedrė Mockevičienė,
Valérie Schwitzgebel
Publication year - 2020
Publication title -
diabetes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.219
H-Index - 330
eISSN - 1939-327X
pISSN - 0012-1797
DOI - 10.2337/db19-0974
Subject(s) - diabetes mellitus , medicine , candidate gene , gene , genetics , biology , endocrinology
Identifying gene variants causing monogenic diabetes (MD) increases understanding of disease etiology and allows for implementation of precision therapy to improve metabolic control and quality of life. Here, we aimed to assess the prevalence of MD in youth with diabetes in Lithuania, uncover potential diabetes-related gene variants, and prospectively introduce precision treatment. First, we assessed all pediatric and most young-adult patients with diabetes in Lithuania ( n = 1,209) for diabetes-related autoimmune antibodies. We then screened all antibody-negative patients ( n = 153) using targeted high-throughput sequencing of >300 potential candidate genes. In this group, 40.7% had MD, with the highest percentage (100%) in infants (diagnosis at ages 0-12 months), followed by those diagnosed at ages >1-18 years (40.3%) and >18-25 years (22.2%). The overall prevalence of MD in youth with diabetes in Lithuania was 3.5% (1.9% for GCK diabetes, 0.7% for HNF1A , 0.2% for HNF4A and ABCC8 , 0.3% for KCNJ11 , and 0.1% for INS ). Furthermore, we identified likely pathogenic variants in 11 additional genes. Microvascular complications were present in 26% of those with MD. Prospective treatment change was successful in >50% of eligible candidates, with C-peptide >252 pmol/L emerging as the best prognostic factor.
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