Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models
Open accessAbstract
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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Integration of multi-omics datasets with drug usage information has been used to identify drug-associated individual omics features, quantifying drug response effects.
“The integration of multi-omics datasets with the drug usage information has been used to identify drug-associated individual omics features, quantifying the drug response effects [18].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [18]
Froguel et al. introduced the MOVE framework for identifying associations between drug usage and multi-omics data features, helping characterize drug effects on type 2 diabetes.
“Additionally, Froguel et al. introduced a multi-omics variational autoencoders (MOVE) framework for identifying the associations between drug usage and multi-omics data features, helping characterize the drug effects on type 2 diabetes [18].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [18]
The MOVE framework utilized a perturbation-based approach to identify significant drug-related multimodal features in type 2 diabetes, providing a framework for interpreting biomedical multimodal datasets.
“The MOVE framework utilized a perturbation-based approach to identify significant drug-related multimodal features in type 2 diabetes, providing a framework for interpreting biomedical multimodal datasets [18].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [18]
The perturbation-based method has been effective in characterizing the most drug-responsive omics features.
“Although the perturbation-based method has been effective in characterizing the most drug-responsive omics features [18], there is still a need for further research and discussions to improve cross-modal interpretation by refining model designs.”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [18]
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Surendran, A., Neale, Q., Zahedi, R. P., & Ravandi, A. (2026) · Expert Review of Proteomics · doi:10.1080/14789450.2026.2644214
- Authors
- Allesøe, R. L., Lundgaard, A. T., Hernández Medina, R., Aguayo-Orozco, A., Johansen, J., Nissen, J. N., Brorsson, C., Mazzoni, G., Niu, L., Biel, J. H., Leal Rodríguez, C., Brasas, V., Webel, H., Benros, M. E., Pedersen, A. G., Chmura, P. J., Jacobsen, U. P., Mari, A., Koivula, R., Mahajan, A., Vinuela, 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., 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, Froguel, P., Thomas, C. E., 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., Stærfeldt, 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, N. A., 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, P. W., Nilsson, B., Tsirigos, K., Eriksen, R., Jablonka, B., Uhlen, M., Gassenhuber, J., Baltauss, T., de Preville, N., Klintenberg, M., & Abdalla, M.
- Affiliations
- University of Copenhagen (DK)
- Technical University of Denmark (DK)
- Centre for Human Genetics (GB)
- Helmholtz Munich (DE)
- Science for Life Laboratory (SE)
- Sanofi (Germany) (DE)
- University of Exeter (GB)
- University of Dundee (GB)
- University of Westminster (GB)
- Imperial College London (GB)
- Herlev Hospital (DK)
- Amsterdam Public Health (NL)
- Inserm (FR)
- University of Eastern Finland (FI)
- Newcastle University (GB)
- Royal Victoria Infirmary (GB)
- University of Oxford (GB)
- Lund University (SE)
- Eli Lilly (United States) (US)
- University of Geneva (CH)
- Harvard University (US)
- University of Ljubljana (SI)
- Institut de Recherches Internationales Servier (France) (FR)
- Royal Devon & Exeter NHS Foundation Trust (GB)
- Boehringer Ingelheim (Germany) (DE)
- University of Bath (GB)
- Saarland University (DE)
- Leiden University Medical Center (NL)
- Universität Ulm (DE)
- Eli Lilly (Germany) (DE)
- University of Tübingen (DE)
- Centro de Investigación Biomédica en Red Diabetes y Enfermedades Metabólicas Asociadas (ES)
- Corresponding author
- Rasmussen, Simon
- Brunak, Søren
- ORCID
- Allesøe, Rosa Lundbye
- Lundgaard, Agnete Troen
- Hernández Medina, Ricardo
- 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
- Sharma, Sapna
- Haid, Mark
- Hong, Mun-Gwan
- Vogt, Josef
- Jones, Angus
- Kennedy, Gwen
- Bell, Jimmy
- Thomas, E. Louise
- Frost, Gary
- Thomsen, Henrik
- Hansen, Tue Haldor
- Vestergaard, Henrik
- Muilwijk, Mirthe
- Blom, Marieke T.
- Raverdy, Violeta
- 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
- Pedersen, Oluf
- Hansen, Torben
- Franks, Paul W.
- Schwenk, Jochen M.
- Adamski, Jerzy
- Pearson, Ewan
- Banasik, Karina
- Rasmussen, Simon
- Brunak, Søren
- Froguel, Philippe
- Thomas, Cecilia Engel
- Beulens, Joline
- Rutters, Femke
- Nijpels, Giel
- Groeneveld, Lenka
- Elders, Petra
- Bianzano, Susanna
- Hennige, Anita
- Grempler, Rolf
- Baum, Patrick
- Shah, Nisha
- Teare, Harriet
- Ehrhardt, Beate
- Tillner, Joachim
- Dings, Christiane
- Lehr, Thorsten
- Bizzotto, Roberto
- Dekkers, Koen
- Slieker, Roderick
- Ramisch, Anna
- Jennison, Christopher
- Frau, Francesca
- Adragni, Kofi
- Thomas, Melissa
- Fitipaldi, Hugo
- Kurbasic, Azra
- Mutie, Pascal
- Caiazzo, Robert
- Holl, Reinhard
- Kuulasmaa, Teemu
- Deshmukh, Harshal
- Cederberg, Henna
- Laakso, Markku
- Vangipurapu, Jagadish
- Dale, Matilda
- Thorand, Barbara
- Fritsche, Andreas
- Hudson, Michelle
- Arumugam, Manimozhiyan
- Engelbrechtsen, Line
- Dutta, Avirup
- Fan, Yong
- Brown, Andrew
- Dawed, Adem
- Donnelly, Louise
- White, Margaret
- Ferrer, Jorge
- Whitcher, Brandon
- Prehn, Cornelia
- Adam, Jonathan
- Grallert, Harald
- Gupta, Ramneek
- Sackett, Peter Wad
- Nilsson, Birgitte
- Tsirigos, Konstantinos
- Eriksen, Rebeca
- Gassenhuber, Johann
- Abdalla, Moustafa
- Venue
- Nature Biotechnology
- Published
- 2023-01-02
- Volume
- 41
- Issue
- 3
- Pages
- 399-408
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC10017515
- OpenAlex
- W4313455000
- Cited by
- 111
- References
- 76
- Citations by year
- 2026: 25 · 2025: 35 · 2024: 34 · 2023: 17
- FWCI
- 9.89
- Type
- journal article
- Language
- en
- Update notices
- correction (2023-05-02)
- Open access
- hybrid
- doi.org
- Locations
- Nature Biotechnology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Discovery Research Portal (University of Dundee) (publishedVersion, cc-by)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion, other-oa)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by)
- Repositori digital de la UPF (Universitat Pompeu Fabra) (acceptedVersion, cc-by)
- WestminsterResearch (University of Westminster) (acceptedVersion, cc-by)
- Pure Amsterdam UMC (submittedVersion, cc-by)
- Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (submittedVersion, cc-by)
- Leiden Repository (Leiden University) (submittedVersion, cc-by)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- CNR ExploRA (submittedVersion, cc-by)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion, cc-by)
- HAL (submittedVersion)
- LillOA (Université de Lille (University Of Lille)) (submittedVersion, cc-by)
- Lund University Publications (Lund University) (submittedVersion)
- mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion)
- Apollo (cc-by)
- Apollo
- Funders
- Novo Nordisk Fonden (NNF14CC0001, NNF17OC0027594)
- Innovative Medicines Initiative (115317)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- type 2 diabetes, multi-omics integration, variational autoencoder, metformin, gut microbiota, statins, simvastatin, atorvastatin, polypharmacy, drug-drug similarity
- MeSH terms
- Deep Learning, Algorithms, Diabetes Mellitus, Type 2, Humans
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