MUTATION SPECTRUM OF MONOGENIC DIABETES IN SINGAPORE - Ang, Su Fen; Goh, Li Xian; Chan, Lovynn Wan Ting; Kon, Winston Yin Chian; Vasanwala, Rashida; Loh, Wann Jia; Lim, Su Chi (Journal of the ASEAN Federation of Endocrine Societies, 2022)
OBJECTIVES Monogenic diabetes, a rare condition known to affect about 5% of young-onset diabetes, is not routinely investigated in our local population with diabetes. The NHG-KTPH Monogenic Diabetes Registry was set up in 2017 to study the prevalence of monogenic diabetes in Singapore as well as determine the underlying mutations responsible for this condition to facilitate the application of precision medicine to this group of individuals. METHODOLOGY Young-onset (≤35 years) individuals with atypical diabetes were enrolled into our monogenic diabetes study and subjected to genetic testing using 16-gene nextgeneration sequencing, mt.3243A>G TaqMan genotyping and multiplex-ligation dependent probe amplification (for HNF1A, HNF4A, GCK, HNF1B). Variants identified were annotated according to guidelines from American College of Medical Genetics and Genomics (ACMG). Likely pathogenic/pathogenic variants were validated using bidirectional Sanger sequencing. RESULTS Among 340 probands sequenced, 43 (12.6%) had a likely pathogenic/pathogenic variant in one of these 8 genes: HNF1A (27.9%), HNF4A (25.6%), GCK (16.3%), mt.3243A>G (16.3%), HNF1B (4.7%), ABCC8 (4.7%), PAX4 (2.3%) and NEUROD1 (2.3%). Most (95.3%) of the variants occurred in genes which are clinically actionable. Mutation-positive cases are mostly female (67.4% vs 49.8%, p=0.031), have lower BMI (median 24.9 vs 22.4 kg/m2, p<0.001), lower waist circumference (74.0 vs 82.0 cm, p<0.001), higher HDL (1.41 vs 1.26 mM, p=0.003), lower triglycerides (0.95 vs 1.39 mM, p=0.005), lower C-peptide (506.2 vs 819.8 pM, p=0.013), lower hsCRP (0.70 vs 1.40 mg/L, p=0.007) and lower uric acid (5.45 vs 6.41 mg/dL, p=0.007) than mutation-negative cases. CONCLUSION Monogenic diabetes is non-trivial in our local population of young-onset atypical diabetes. The combination of clinical parameters and biomarkers can be explored to improve selection of individuals for genetic testing. This facilitates early and accurate genetic diagnosis of monogenic diabetes that can better inform clinical management.