Polygenic score from MODY genes is associated with type 1 diabetes and disease characteristics
Year: 2025
Type: article
Study design: Observational Study
Abstract: AIMS: This study evaluates the contribution of common variants in Maturity-Onset Diabetes of the Young (MODY) genes on type 1 diabetes (T1D), using a polygenic score (PGS) approach. METHODS: 485 children and youth diagnosed with T1D from at least 1 year and 271 healthy controls (HC) were recruited. Personal information (i.e. age, sex, height, weight) were collected for each participant, and clinical information (i.e. age at diagnosis, disease duration, presence of autoantibodies and ketoacidosis at onset (DKA)) were also obtained for T1D subjects. Participants were genotyped using Illumina Infinium Global Screening Array. PGS based on Single Nucleotide Polymorphisms (SNPs) in 16 MODY genes were developed. The association of this PGS with T1D susceptibility and clinical disease characteristics was assessed by regression analysis. RESULTS: A PGS including 335 SNPs in MODY genes discriminates T1D from HC (AUC = 60.1%, AIC = 787.6). This PGS was significantly higher in T1D compared to HC (p-value = 0.0004, pseudo-R2 = 2.85%). Moreover, regression analysis between PGS and T1D clinical characteristics showed higher PGS values in T1D subjects with zinc transporter 8 autoantibodies (ZnT8A) compared with T1D subjects without ZnT8A (p-value = 0.04). A similar trend was also observed for antibodies directed against glutamic acid decarboxylase (GADA), although the association did not reach statistical significance (p-value = 0.06). CONCLUSIONS: Our study suggests that a polygenic approach based on MODY genes may discriminate T1D from HC and may contribute to patient stratification, helping to better understand T1D heterogeneity.
Source: Acta Diabetologica
Institutions: IRCCS Materno Infantile Burlo Garofolo, Ospedale Santa Chiara, University of Ljubljana, Ljubljana University Medical Centre, IRCCS Ospedale San Raffaele, Istituto di Ricovero e Cura a Carattere Scientifico San Raffaele, Azienda Ospedaliera Citta' della Salute e della Scienza di Torino, University of Campania "Luigi Vanvitelli", Università degli Studi del Piemonte Orientale “Amedeo Avogadro”, University of Trieste
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $4,590
Awards: Endogenous viral elements in type 1 diabetes pathogenesis, G2185715034, G5156598801, The use of autonomous artificial intelligence for the detection of early signs of retinal damage and association with long-term fluctuations in glucose levels in children and youth with type 1 diabetes, Etiology, early detection and treatment of diseases in childhood and adolescence
OpenAlex ID: W4411497184
