Clinical usefulness of multigene screening with phenotype-driven bioinformatics analysis for the diagnosis of patients with monogenic diabetes or severe insulin resistance. (November 2020)

• Record Type: Journal Article
• 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
• Is Part Of: Diabetes research and clinical practice. Volume 169(2020)
• Publication Date: 2020-11
• Subjects: Genetic diagnosis -- Monogenic diabetes -- Maturity-onset diabetes of the young -- Severe insulin resistance -- Next-generation sequencing; Diabetes -- Periodicals; Diabetes Mellitus -- Periodicals; 616.462
• DOI: 10.1016/j.diabres.2020.108461
• Languages: English
• ISSNs: 0168-8227

Abstract

Abstract: Aims: 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. Methods: 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. Results: 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. Conclusions: This comprehensive approach could assist in the early diagnosis of patients with monogenic diabetes and facilitate the provision of tailored therapy.