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Multiâomics analysis reveals drivers of loss of β âcell function after newly diagnosed autoimmune type 1 diabetes: An INNODIA multicenter study
Publication status: PublishedFunder: Innovative Medicine Initiative 2 Joint UndertakingFunder: European Federation of Pharmaceutical Industries and Associations; doi: http://dx.doi.org/10.13039/100013322Funder: European Union's Horizon 2020 research and innovation programFunder: Leona M. and Harry B. Helmsley Charitable Trust; doi: http://dx.doi.org/10.13039/100007028Funder: JDRF; doi: http://dx.doi.org/10.13039/100022690Aims: Heterogeneity in the rate of βâcell loss in newly diagnosed type 1 diabetes patients is poorly understood and creates a barrier to designing and interpreting diseaseâmodifying clinical trials. Integrative analyses of baseline multiâomics data obtained after the diagnosis of type 1 diabetes may provide mechanistic insight into the diverse rates of disease progression after type 1 diabetes diagnosis. Methods: We collected samples in a panâEuropean consortium that enabled the concerted analysis of five different omics modalities in data from 97 newly diagnosed patients. In this study, we used MultiâOmics Factor Analysis to identify molecular signatures correlating with postâdiagnosis decline in βâcell mass measured as fasting Câpeptide. Results: Two molecular signatures were significantly correlated with fasting Câpeptide levels. One signature showed a correlation to neutrophil degranulation, cytokine signalling, lymphoid and nonâlymphoid cell interactions and Gâprotein coupled receptor signalling events that were inversely associated with a rapid decline in βâcell function. The second signature was related to translation and viral infection was inversely associated with change in βâcell function. In addition, the immunomics data revealed a Natural Killer cell signature associated with rapid βâcell decline. Conclusions: Features that differ between individuals with slow and rapid decline in βâcell mass could be valuable in staging and prediction of the rate of disease progression and thus enable smarter (shorter and smaller) trial designs for disease modifying therapies as well as offering biomarkers of therapeutic effect