A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Transcriptional networks in at-risk individuals identify signatures of type 1 diabetes progression
Tekijät: Xhonneux Louis-Pascal, Knight Oliver, Lernmark Åke, Bonifacio Ezio, Hagopian William A, Rewers Marian J, She Jin-Xiong, Toppari Jorma, Parikh Hemang, Smith Kenneth GC, Ziegler Anette-G, Akolkar Beena, Krischer Jeffrey P, McKinney Eoin F
Kustantaja: AMER ASSOC ADVANCEMENT SCIENCE
Julkaisuvuosi: 2021
Journal: Science Translational Medicine
Tietokannassa oleva lehden nimi: SCIENCE TRANSLATIONAL MEDICINE
Lehden akronyymi: SCI TRANSL MED
Artikkelin numero: ARTN eabd5666
Vuosikerta: 13
Numero: 587
Sivujen määrä: 15
ISSN: 1946-6234
eISSN: 1946-6242
DOI: https://doi.org/10.1126/scitranslmed.abd5666
Verkko-osoite: https://stm.sciencemag.org/content/13/587/eabd5666
Rinnakkaistallenteen osoite: https://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC8447843&blobtype=pdf
Type 1 diabetes (T1D) is a disease of insulin deficiency that results from autoimmune destruction of pancreatic islet. 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.
