Etiology-based diagnosis of pediatric patients with atypical diabetes using routine and omic-based phenotyping and genotyping

Abstract

Introduction: Among the two main forms of diabetes (type 1 and 2), rare subtypes of the disease called monogenic diabetes (MODY) are hardly diagnosed because of their resemblance to them. This may deprive the patient of an appropriate treatment, which could be simplified (oral antidiabetics replacing insulin) or multidisciplinary. Objectives: Improve the diagnosis of atypical forms of diabetes with the creation of a predictive score. Methods: A Belgian study consortium was created to screen, using routine diagnostic tools, for monogenic forms of diabetes in pediatric patients followed for diabetes. We compiled the most representative clinical features of monogenic diabetes (e.g. lack of anti-islet antibodies, residual C-peptide secretion and low glycemic variability; features not considered by the MODY calculator) into a new diagnostic tool, the DIAMODIA score. Patients enrolled were phenotyped (e.g. glycemic variability, multiplex serum protein assays) and patients fulfilling sufficient criteria were genotyped (whole-exome sequencing using NGS). Gene-phenotype correlations were performed using bioinformatics. Results: A cohort of 446 patients diagnosed with diabetes was evaluated. Our DIAMODIA score identified a subgroup of 109 patients likely to present atypical diabetes. Routine MODY gene panel analysis identified 34 patients with class 5 variant (the “mody” cohort). The in-depth WES analysis identified 37 ADia patients (the “ADia cohort”) with class 3 variant, providing our DIAMODIA score a yield of 31% for class 5 and 34% for class 3 variant positivity. Data derived from the ADia cohort confirmed the following characteristics: absence of anti-islet antibodies, residual C-peptide secretion. Also, low levels of glycemic variability were key in identifying ADia patients with variants. DIAMODIA score was revised to present the variables most predictive of having a potentiel variant. Conclusions: Phenotyping and genotyping helped us decipher new variables and molecular elements in patients with atypical diabetes