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Allesøe, R.L.* ; Lundgaard, A.T.* ; Hernández Medina, R.* ; Aguayo-Orozco, A.* ; Johansen, J.D.* ; Nissen, J.N.* ; Brorsson, C.* ; Mazzoni, G.* ; Niu, L.* ; Biel, J.H.* ; Brasas, V.* ; Webel, H.* ; Benros, M.E.* ; Pedersen, A.G.* ; Chmura, P.J.* ; Jacobsen, U.P.* ; Mari, A.* ; Koivula, R.W.* ; Mahajan, A.* ; Viñuela, A.* ; Tajes, J.F.* ; Sharma, S. ; Haid, M. ; Hong, M.G.* ; Musholt, P.B.* ; De Masi, F.* ; Vogt, J.* ; Pedersen, H.K.* ; Gudmundsdottir, V.* ; Jones, A.* ; Kennedy, G.* ; Bell, J.* ; Thomas, E.L.* ; Frost, G.* ; Thomsen, H.* ; Hansen, E.* ; Hansen, T.H.* ; Vestergaard, H.* ; Muilwijk, M.* ; Blom, M.T.* ; 't Hart, L.M.* ; Pattou, F.* ; Raverdy, V.* ; Brage, S.* ; Kokkola, T.* ; Heggie, A.* ; McEvoy, D.* ; Mourby, M.* ; Kaye, J.* ; Hattersley, A.* ; McDonald, T.A.* ; Ridderstråle, M.* ; Walker, M.* ; Forgie, I.* ; Giordano, G.N.* ; Pavo, I.* ; Ruetten, H.* ; Pedersen, O.* ; Hansen, T.* ; Dermitzakis, E.* ; Franks, P.W.* ; Schwenk, J.M.* ; Adamski, J. ; McCarthy, M.I.* ; Pearson, E.* ; Banasik, K.* ; Rasmussen, S.* ; Brunak, S.* ; IMI DIRECT Consortium (Thorand, B. ; Fritsche, A. ; Artati, A. ; Prehn, C. ; Grallert, H. ; Adam, J.)

Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models.

Nat. Biotechnol. 41, 399-408 (2023)
Postprint Forschungsdaten DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
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.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Metformin Treatment; Multi-omics; Metaanalysis; Cholesterol
Sprache englisch
Veröffentlichungsjahr 2023
HGF-Berichtsjahr 2023
ISSN (print) / ISBN 1087-0156
e-ISSN 1546-1696
Zeitschrift Nature Biotechnology
Quellenangaben Band: 41, Heft: 3, Seiten: 399-408 Artikelnummer: , Supplement: ,
Verlag Nature Publishing Group
Verlagsort New York, NY
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Epidemiology (EPI)
CF Metabolomics & Proteomics (CF-MPC)
Institute of Experimental Genetics (IEG)
Institute of Diabetes Research and Metabolic Diseases (IDM)
POF Topic(s) 30202 - Environmental Health
30505 - New Technologies for Biomedical Discoveries
30201 - Metabolic Health
90000 - German Center for Diabetes Research
Forschungsfeld(er) Genetics and Epidemiology
Enabling and Novel Technologies
Helmholtz Diabetes Center
PSP-Element(e) G-504091-002
A-630710-001
G-500600-001
G-504000-002
G-502400-001
G-504091-004
Förderungen Innovative Medicines Initiative
Novo Nordisk Fonden
Seventh Framework Programme
DOI 10.1038/s41587-022-01520-x
WOS ID 000909067600016
Scopus ID 85145508974
PubMed ID 36593394
Erfassungsdatum 2023-03-13
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