Clinical usefulness of multigene screening with phenotype-driven bioinformatics analysis for the diagnosis of patients with monogenic diabetes or severe insulin resistance

• Published in: Diabetes research and clinical practice Vol. 169; p. 108461
• Main Authors: Hosoe, Jun, Miya, Fuyuki, Kadowaki, Hiroko, Fujiwara, Toyofumi, Suzuki, Ken, Kato, Takashi, Waki, Hironori, Sasako, Takayoshi, Aizu, Katsuya, Yamamura, Natsumi, Sasaki, Fusako, Kurano, Makoto, Hara, Kazuo, Tanaka, Masaki, Ishiura, Hiroyuki, Tsuji, Shoji, Honda, Kenjiro, Yoshimura, Jun, Morishita, Shinichi, Matsuzawa, Fumiko, Aikawa, Sei-Ichi, Boroevich, Keith A., Nangaku, Masaomi, Okada, Yukinori, Tsunoda, Tatsuhiko, Shojima, Nobuhiro, Yamauchi, Toshimasa, Kadowaki, Takashi
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 01-11-2020
• Subjects: Adolescent; Adult; Aged; Computational Biology; Diabetes Mellitus - diagnosis; Diabetes Mellitus - genetics; Female; Genetic diagnosis; Genetic Testing - methods; Genotype; GTPase-Activating Proteins - genetics; High-Throughput Nucleotide Sequencing; Humans; Infant; Insulin Resistance - genetics; Japan; Male; Mass Screening - methods; Maturity-onset diabetes of the young; Middle Aged; Monogenic diabetes; Mutation, Missense; Next-generation sequencing; Phenotype; Severe insulin resistance; Young Adult; Japan; Severe insulin resistance; Maturity-onset diabetes of the young; Next-generation sequencing; Monogenic diabetes; Genetic diagnosis
• ISSN: 0168-8227; 1872-8227
• DOI: 10.1016/j.diabres.2020.108461

Monogenic diabetes is clinically heterogeneous and differs from common forms of diabetes (type 1 and 2). We aimed to investigate the clinical usefulness of a comprehensive genetic testing system, comprised of targeted next-generation sequencing (NGS) with phenotype-driven bioinformatics analysis in patients with monogenic diabetes, which uses patient genotypic and phenotypic data to prioritize potentially causal variants.
We performed targeted NGS of 383 genes associated with monogenic diabetes or common forms of diabetes in 13 Japanese patients with suspected (n = 10) or previously diagnosed (n = 3) monogenic diabetes or severe insulin resistance. We performed in silico structural analysis and phenotype-driven bioinformatics analysis of candidate variants from NGS data.
Among the patients suspected having monogenic diabetes or insulin resistance, we diagnosed 3 patients as subtypes of monogenic diabetes due to disease-associated variants of INSR, LMNA, and HNF1B. Additionally, in 3 other patients, we detected rare variants with potential phenotypic effects. Notably, we identified a novel missense variant in TBC1D4 and an MC4R variant, which together may cause a mixed phenotype of severe insulin resistance.
This comprehensive approach could assist in the early diagnosis of patients with monogenic diabetes and facilitate the provision of tailored therapy.