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Background: Development of disease-modifying therapy for type 1 diabetes (T1D) is hampered by the limited understanding of pathogenesis, heterogeneity, lack of disease biomarkers and stratifiers. Using the INNODIA consortium pan-European infrastructure to collect prospective clinical data from newly diagnosed subjects combined with multi-omics data, we mined integrated datasets using deep learning to identify novel relationships that could transform the disease monitoring landscape. Method: Samples collected Results: Clustering identified five age-independent subgroups with significantly different progression patterns (p Discussion: The preliminary results suggest that the VAE can learn meaningful progression patterns from integrated clinical and multi-omics data, which holds great promise to uncover important data structures not readily discovered when analyzing single data types. Disclosure C. Brorsson: Employee; Spouse/Partner; Novo Nordisk A/S. J. Almagro armenteros: None. G. Mazzoni: Employee; Self; Novo Nordisk A/S. S. Kaur: None. A. M. Schulte: None. C. Mathieu: Advisory Panel; Self; Novo Nordisk, Sanofi, Merck Sharp and Dohme Ltd., Eli Lilly and Company, Novartis, AstraZeneca, Boehringer Ingelheim, Roche, Medtronic, ActoBio Therapeutics, Pfizer, Insulet and Zealand Pharma, Research Support; Self; Medtronic, Novo Nordisk, Sanofi and ActoBio Therapeutics, Speaker’s Bureau; Self; Novo Nordisk, Sanofi, Eli Lilly and Company, Boehringer Ingelheim, AstraZeneca and Novartis. S. Brunak: Board Member; Self; Intomics A/S, Proscion A/S, Stock/Shareholder; Self; Hoba Therapeutics Aps, Novo Nordisk A/S. On behalf of the innodia consortium: n/a. Funding INNODIA (115797)