Discovery of drug–omics associations in type 2 diabetes with generative deep-learning models
Authors: Allesøe Rosa Lundbye , Lundgaard Agnete Troen , Hernández Medina Ricardo , Aguayo-Orozco Alejandro , Johansen Joachim , Nissen Jakob Nybo , Brorsson Caroline , Mazzoni Gianluca , Niu Lili , Biel Jorge Hernansanz , Leal Rodríguez Cristina , Brasas Valentas , Webel Henry , Benros Michael Eriksen , Pedersen Anders Gorm , Chmura Piotr Jaroslaw , Jacobsen Ulrik Plesner , Mari Andrea , Koivula Robert , Mahajan Anubha , Vinuela Ana , Tajes Juan Fernandez , Sharma Sapna , Haid Mark , Hong Mun-Gwan , Musholt Petra B. , De Masi Federico , Vogt Josef , Pedersen Helle Krogh , Gudmundsdottir Valborg , Jones Angus , Kennedy Gwen , Bell Jimmy , Thomas E. Louise , Frost Gary , Thomsen Henrik , Hansen Elizaveta , Hansen Tue Haldor , Vestergaard Henrik , Muilwijk Mirthe , Blom Marieke T. , ‘t Hart Leen M. , Pattou Francois , Raverdy Violeta , Brage Soren , Kokkola Tarja , Heggie Alison , McEvoy Donna , Mourby Miranda , Kaye Jane , Hattersley Andrew , McDonald Timothy , Ridderstråle Martin , Walker Mark , Forgie Ian , Giordano Giuseppe N. , Pavo Imre , Ruetten Hartmut , Pedersen Oluf , Hansen Torben , Dermitzakis Emmanouil , Franks Paul W. , Schwenk Jochen M. , Adamski Jerzy , McCarthy Mark I. , Pearson Ewan , Banasik Karina , Rasmussen Simon , Brunak Søren ,   IMI DIRECT Consortium, Froguel Philippe , Thomas Cecilia Engel , Haussler Ragna , Beulens Joline , Rutters Femke , Nijpels Giel , van Oort Sabine , Groeneveld Lenka , Elders Petra , Giorgino Toni , Rodriquez Marianne , Nice Rachel , Perry Mandy , Bianzano Susanna , Graefe-Mody Ulrike , Hennige Anita , Grempler Rolf , Baum Patrick , Stærfeldt Hans-Henrik , Shah Nisha , Teare Harriet , Ehrhardt Beate , Tillner Joachim , Dings Christiane , Lehr Thorsten , Scherer Nina , Sihinevich Iryna , Cabrelli Louise , Loftus Heather , Bizzotto Roberto , Tura Andrea , Dekkers Koen , van Leeuwen Nienke , Groop Leif , Slieker Roderick , Ramisch Anna , Jennison Christopher , McVittie Ian , Frau Francesca , Steckel-Hamann Birgit , Adragni Kofi , Thomas Melissa , Pasdar Naeimeh Atabaki , Fitipaldi Hugo , Kurbasic Azra , Mutie Pascal , Pomares-Millan Hugo , Bonnefond Amelie , Canouil Mickael , Caiazzo Robert , Verkindt Helene , Holl Reinhard , Kuulasmaa Teemu , Deshmukh Harshal , Cederberg Henna , Laakso Markku , Vangipurapu Jagadish , Dale Matilda , Thorand Barbara , Nicolay Claudia , Fritsche Andreas , Hill Anita , Hudson Michelle , Thorne Claire , Allin Kristine , Arumugam Manimozhiyan , Jonsson Anna , Engelbrechtsen Line , Forman Annemette , Dutta Avirup , Sondertoft Nadja , Fan Yong , Gough Stephen , Robertson Neil , McRobert Nicky , Wesolowska-Andersen Agata , Brown Andrew , Davtian David , Dawed Adem , Donnelly Louise , Palmer Colin , White Margaret , Ferrer Jorge , Whitcher Brandon , Artati Anna , Prehn Cornelia , Adam Jonathan , Grallert Harald , Gupta Ramneek , Sackett Peter Wad , Nilsson Birgitte , Tsirigos Konstantinos , Eriksen Rebeca , Jablonka Bernd , Uhlen Mathias , Gassenhuber Johann , Baltauss Tania , de Preville Nathalie , Klintenberg Maria , Abdalla Moustafa 
Published: January 2023

AbstractThe application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug–omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug–drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities.