ESPE Abstracts (2016) 86 RFC9.3

Molecular Analysis of a Large Cohort of MODY Patients by Next Generation Sequencing

Rosangela Artusoa, Valerio Orlandinib, Viviana Palazzob, Laura Giuntia, Samuela Landinib, Aldesia Provenzanob, Andrea La Barberaa, Sabrina Giglioa & Stefano Stagic


aMedical Genetics Unit, Meyer Children’s University Hospital, Florence, Italy; bMedical Genetics Unit, Department of Clinical and Experimental Biomedical Sciences ‘Mario Serio’, University of Florence, Florence, Italy; cHealth Sciences Department, University of Florence, Anna Meyer Children’s University Hospital, Florence, Italy


Background: Maturity-onset diabetes of the young (MODY) is a monogenic form of diabetes that accounts for 2–5% of all cases but it is underestimated because it’s often misdiagnosed as T1D or T2D whose symptoms are often overlapping. It is a phenotypically and genetically heterogeneous disorder characterised by autosomal dominant inheritance, a young age of onset and pancreatic β-cell dysfunction.

Objective and hypotheses: Actually in about 50% of MODY patients is not identified causative mutations in known genes (MODYx). Recent advances in next-generation sequencing technologies make it affordable to search for rare and functional variants for common complex diseases systematically. On the bases of this observation, we decide to analyse 100 MODY patients through next generation sequencing (NGS) tecnology.

Method: A set of 182 genes were chosen for targeted resequencing (454 Roche platform). We selected genes known implicated in the pathway of pancreatic β cells, candidate genes for T2D, and genes causative of diabetes in mice experiments.

Results: In the 66% of cases we found, in association with known heterozygous/homozygous SNPs associated with diabetes, rare and pathogenetic variants, demonstrating that this approach leads to a genetic diagnosis in most of patients. Moreover, two mutations were identified in different genes in 40% of cases suggesting a complex etiology.

Conclusion: The increased number of genes tested led to a higher mutation detection rate. This approach may help in understanding the molecular aetiology of diabetes and in providing a more personalised treatment for each genetic subtype.

Article tools

My recent searches

No recent searches.