Integrative analyses of TEDDY Omics data reveal lipid metabolism abnormalities, increased intracellular ROS and heightened inflammation prior to autoimmunity for type 1 diabetes

The Environmental Determinants of Diabetes in the Young (TEDDY) is a prospective birth cohort designed to study type 1 diabetes (T1D) by following children with high genetic risk. An integrative multi-omics approach was used to evaluate islet autoimmunity etiology, identify disease biomarkers, and u...

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Veröffentlicht in:Genome Biology 2021-01, Vol.22 (1), p.39-39, Article 39
Hauptverfasser: Balzano-Nogueira, Leandro, Ramirez, Ricardo, Zamkovaya, Tatyana, Dailey, Jordan, Ardissone, Alexandria N, Chamala, Srikar, Serrano-Quílez, Joan, Rubio, Teresa, Haller, Michael J, Concannon, Patrick, Atkinson, Mark A, Schatz, Desmond A, Triplett, Eric W, Conesa, Ana
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Sprache:eng
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Zusammenfassung:The Environmental Determinants of Diabetes in the Young (TEDDY) is a prospective birth cohort designed to study type 1 diabetes (T1D) by following children with high genetic risk. An integrative multi-omics approach was used to evaluate islet autoimmunity etiology, identify disease biomarkers, and understand progression over time. We identify a multi-omics signature that was predictive of islet autoimmunity (IA) as early as 1 year before seroconversion. At this time, abnormalities in lipid metabolism, decreased capacity for nutrient absorption, and intracellular ROS accumulation are detected in children progressing towards IA. Additionally, extracellular matrix remodeling, inflammation, cytotoxicity, angiogenesis, and increased activity of antigen-presenting cells are observed, which may contribute to beta cell destruction. Our results indicate that altered molecular homeostasis is present in IA-developing children months before the actual detection of islet autoantibodies, which opens an interesting window of opportunity for therapeutic intervention. The approach employed herein for assessment of the TEDDY cohort showcases the utilization of multi-omics data for the modeling of complex, multifactorial diseases, like T1D.
ISSN:1474-760X
1474-7596
1474-760X
DOI:10.1186/s13059-021-02262-w