1402-P: Novel Lipodystrophy Genotype Correlations Identified through the Rare and Atypical Diabetes Network (RADIANT)

Introduction: Lipodystrophy (LD) encompasses a group of heterogeneous disorders characterized by either generalized or partial lack of adipose tissue. Discovery of the genetic etiologies of LD has advanced understanding of the molecular mechanisms that govern fat-cell differentiation, maintenance, and function. Currently, more than 15 different genes are known to cause distinct forms of LD; however, the genetic etiology of over 50% of familial partial and about 30% of congenital generalized LD cases remains unknown.

Methods: We utilized the nationwide NIDDK-initiated Rare and Atypical Diabetes Network (RADIANT) to genetically characterize 20 cases of LD with unknown molecular etiology using clinical-grade (CLIA) whole genome sequencing (WGS).

Results: Most participants were White (18/20) and female (18/20), with a mean age at diagnosis of 31 + 18 years, mean BMI of 29.7 + 9.7 kg/m2, mean HbA1c of 9.7 + 2.7%, and mean triglyceride level of 172.3 + 94.7 mg/dL (range 55-388 mg/dL). Eighteen cases presented with partial LD and two with generalized LD. Genome sequencing and analysis led to the identification of candidate rare variants in genes that can plausibly be linked to development of the lipodystrophy phenotype (e.g. SIN3A, DPP4, CERS2, IRS2, LMNTD2 or SMAD5) in addition to variants in known or closely related pathway genes (LMNA, PLIN5, LIPE, ACADVL). Candidate variants prompting further investigations were identified in 18 of the 20 cases. In early downstream analyses, we were able to show genotype segregation of suspected variants with overall phenotype in two families with distinctive atypical partial lipodystrophy.

Conclusions: These 20 cases demonstrate preliminary efforts to recruit and investigate novel and atypical forms of LD. Further genotyping, phenotyping and functional studies are anticipated to establish causality. Lastly, we will enhance recruitment efforts to include additional cases with a diverse background to enhance generalizability.