Gene expression signature predicts rate of type 1 diabetes progression.

ABSTRACT:

Background

Type 1 diabetes is a complex heterogenous autoimmune disease without therapeutic interventions available to prevent or reverse the disease. This study aimed to identify transcriptional changes associated with the disease progression in patients with recent-onset type 1 diabetes.

Methods

Whole-blood samples were collected as part of the INNODIA study at baseline and 12 months after diagnosis of type 1 diabetes. We used linear mixed-effects modelling on RNA-seq data to identify genes associated with age, sex, or disease progression. Cell-type proportions were estimated from the RNA-seq data using computational deconvolution. Associations to clinical variables were estimated using Pearson's or point-biserial correlation for continuous and dichotomous variables, respe

SUBMITTER: Suomi T 

PROVIDER: S-EPMC10277927 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature