👤 Nobuhiro Shojima

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. Show less

The aim of the present study was to explore the role of variations with modest effects (previously identified by a large-scale meta-analysis in European populations) in the genetic background of type 2 diabetes (T2D) and diabetes-related traits in a Japanese population. We enrolled 2632 Japanese subjects with T2D and 2050 non-diabetic subjects. We analyzed nine single-nucleotide polymorphisms (SNPs), including rs340874 (PROX1), rs4607517 (GCK), rs2191349 (DGKB-TMEM195), rs7034200 (GLIS3), rs10885122 (ADRA2A), rs174550 (FADS1), rs11605924 (CRY2), rs10830963 (MTNR1B) and rs35767 (IGF1). rs340874 (PROX1) and rs174550 (FADS1) were significantly associated with T2D (P=0.0078, OR: 1.12; and P=0.0071, OR: 1.12, respectively). Subjects with more risk alleles related to nine SNPs had an increased risk of T2D (P=0.0017), as well as a higher fasting plasma glucose level (P=0.018), higher HbA(1c) level (P=0.013) and lower HOMA-β (P=0.033) compared with subjects who had fewer risk alleles. We identified a significant association of a SNP of FADS1 and a SNP near PROX1 with T2D in a Japanese population. The present findings suggest that inclusion of SNPs with a tendency to increase the disease risk captured more of the genetic background of T2D than that revealed by only assessing significant SNPs. Show less