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Lookup NU author(s): Dr Ana ViñuelaORCiD, Dr Alison Heggie, Donna McEvoy, Professor Mark Walker
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2023, The Author(s). The 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.
Author(s): Allesoe RL, Lundgaard AT, Hernandez Medina R, Aguayo-Orozco A, Johansen J, Nissen JN, Brorsson C, Mazzoni G, Niu L, Biel JH, Brasas V, Webel H, Benros ME, Pedersen AG, Chmura PJ, Jacobsen UP, Mari A, Koivula R, Mahajan A, Vinuela A, Tajes JF, Sharma S, Haid M, Hong M-G, Musholt PB, De Masi F, Vogt J, Pedersen HK, Gudmundsdottir V, Jones A, Kennedy G, Bell J, Thomas EL, Frost G, Thomsen H, Hansen E, Hansen TH, Vestergaard H, Muilwijk M, Blom MT, 't Hart LM, Pattou F, Raverdy V, Brage S, Kokkola T, Heggie A, McEvoy D, Mourby M, Kaye J, Hattersley A, McDonald T, Ridderstrale M, Walker M, Forgie I, Giordano GN, Pavo I, Ruetten H, Pedersen O, Hansen T, Dermitzakis E, Franks PW, Schwenk JM, Adamski J, McCarthy MI, Pearson E, Banasik K, Rasmussen S, Brunak S, Froguel P, Thomas CE, Haussler R, Beulens J, Rutters F, Nijpels G, van Oort S, Groeneveld L, Elders P, Giorgino T, Rodriquez M, Nice R, Perry M, Bianzano S, Graefe-Mody U, Hennige A, Grempler R, Baum P, Staerfeldt H-H, Shah N, Teare H, Ehrhardt B, Tillner J, Dings C, Lehr T, Scherer N, Sihinevich I, Cabrelli L, Loftus H, Bizzotto R, Tura A, Dekkers K, van Leeuwen N, Groop L, Slieker R, Ramisch A, Jennison C, McVittie I, Frau F, Steckel-Hamann B, Adragni K, Thomas M, Pasdar NA, Fitipaldi H, Kurbasic A, Mutie P, Pomares-Millan H, Bonnefond A, Canouil M, Caiazzo R, Verkindt H, Holl R, Kuulasmaa T, Deshmukh H, Cederberg H, Laakso M, Vangipurapu J, Dale M, Thorand B, Nicolay C, Fritsche A, Hill A, Hudson M, Thorne C, Allin K, Arumugam M, Jonsson A, Engelbrechtsen L, Forman A, Dutta A, Sondertoft N, Fan Y, Gough S, Robertson N, McRobert N, Wesolowska-Andersen A, Brown A, Davtian D, Dawed A, Donnelly L, Palmer C, White M, Ferrer J, Whitcher B, Artati A, Prehn C, Adam J, Grallert H, Gupta R, Sackett PW, Nilsson B, Tsirigos K, Eriksen R, Jablonka B, Uhlen M, Gassenhuber J, Baltauss T, de Preville N, Klintenberg M, Abdalla M, Leal Rodríguez C
Publication type: Article
Publication status: Published
Journal: Nature Biotechnology
Year: 2023
Volume: 41
Pages: 399–408
Online publication date: 02/01/2023
Acceptance date: 20/09/2022
Date deposited: 18/01/2023
ISSN (print): 1087-0156
ISSN (electronic): 1546-1696
Publisher: Springer Nature
URL: https://doi.org/10.1038/s41587-022-01520-x
DOI: 10.1038/s41587-022-01520-x
PubMed id: 36593394
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