Research ResourceMetabolism

Transcriptional networks in at-risk individuals identify signatures of type 1 diabetes progression

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Science Translational Medicine  31 Mar 2021:
Vol. 13, Issue 587, eabd5666
DOI: 10.1126/scitranslmed.abd5666

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Tracking transcripts through to T1D

Type 1 diabetes (T1D) results from insufficient insulin production and is thought to result from an immune reaction against the body’s own pancreatic β cells. Xhonneux et al. analyzed transcriptome patterns in blood samples longitudinally collected from 400 children at high risk for islet autoimmunity and T1D. They detected gene expression changes that tracked with progression to disease onset, including a pre-islet autoimmunity natural killer cell–specific gene signature that was validated in an independent cohort. The microarray data along with covariates such as islet antibody status predicted childhood T1D risk in two independent cohorts, demonstrating the relevance of this dataset to T1D pathobiology.

Abstract

Type 1 diabetes (T1D) is a disease of insulin deficiency that results from autoimmune destruction of pancreatic islet β cells. The exact cause of T1D remains unknown, although asymptomatic islet autoimmunity lasting from weeks to years before diagnosis raises the possibility of intervention before the onset of clinical disease. The number, type, and titer of islet autoantibodies are associated with long-term disease risk but do not cause disease, and robust early predictors of individual progression to T1D onset remain elusive. The Environmental Determinants of Diabetes in the Young (TEDDY) consortium is a prospective cohort study aiming to determine genetic and environmental interactions causing T1D. Here, we analyzed longitudinal blood transcriptomes of 2013 samples from 400 individuals in the TEDDY study before both T1D and islet autoimmunity. We identified and interpreted age-associated gene expression changes in healthy infancy and age-independent changes tracking with progression to both T1D and islet autoimmunity, beginning before other evidence of islet autoimmunity was present. We combined multivariate longitudinal data in a Bayesian joint model to predict individual risk of T1D onset and validated the association of a natural killer cell signature with progression and the model’s predictive performance on an additional 356 samples from 56 individuals in the independent Type 1 Diabetes Prediction and Prevention study. Together, our results indicate that T1D is characterized by early and longitudinal changes in gene expression, informing the immunopathology of disease progression and facilitating prediction of its course.

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