Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models: [with Author Correction]

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models : [with Author Correction]. / 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; 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; Ridderstråle, Martin; Pedersen, Oluf; Hansen, Torben; Banasik, Karina; Rasmussen, Simon; Brunak, Søren; IMI-DIRECT consortium.

In: Nature Biotechnology, Vol. 41, No. 3, 2023, p. 399–408.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Allesøe, RL, Lundgaard, AT, Hernández 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, Ridderstråle, M, Pedersen, O, Hansen, T, Banasik, K, Rasmussen, S, Brunak, S & IMI-DIRECT consortium 2023, 'Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models: [with Author Correction]', Nature Biotechnology, vol. 41, no. 3, pp. 399–408. https://doi.org/10.1038/s41587-022-01520-x

APA

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., Brasas, V., Webel, H., Benros, M. E., Pedersen, A. G., Chmura, P. J., Jacobsen, U. P., Mari, A., Koivula, R., Mahajan, A., ... IMI-DIRECT consortium (2023). Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models: [with Author Correction]. Nature Biotechnology, 41(3), 399–408. https://doi.org/10.1038/s41587-022-01520-x

Vancouver

Allesøe RL, Lundgaard AT, Hernández Medina R, Aguayo-Orozco A, Johansen J, Nissen JN et al. Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models: [with Author Correction]. Nature Biotechnology. 2023;41(3):399–408. https://doi.org/10.1038/s41587-022-01520-x

Author

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 ; 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 ; Ridderstråle, Martin ; Pedersen, Oluf ; Hansen, Torben ; Banasik, Karina ; Rasmussen, Simon ; Brunak, Søren ; IMI-DIRECT consortium. / Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models : [with Author Correction]. In: Nature Biotechnology. 2023 ; Vol. 41, No. 3. pp. 399–408.

Bibtex

@article{88eb674ca6cf4babb529a9e4015dbaec,
title = "Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models: [with Author Correction]",
abstract = "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 = "Alles{\o}e, {Rosa Lundbye} and Lundgaard, {Agnete Troen} and {Hern{\'a}ndez Medina}, Ricardo and Alejandro Aguayo-Orozco and Joachim Johansen and Nissen, {Jakob Nybo} and Caroline Brorsson and Gianluca Mazzoni and Lili Niu and Biel, {Jorge Hernansanz} and Valentas Brasas and Henry Webel and Benros, {Michael Eriksen} and Pedersen, {Anders Gorm} and Chmura, {Piotr Jaroslaw} and Jacobsen, {Ulrik Plesner} and Andrea Mari and Robert Koivula and Anubha Mahajan and Ana Vinuela and Tajes, {Juan Fernandez} and Sapna Sharma and Mark Haid and Mun-Gwan Hong and Musholt, {Petra B} and {De Masi}, Federico and Josef Vogt and Pedersen, {Helle Krogh} and Valborg Gudmundsdottir and Angus Jones and Gwen Kennedy and Jimmy Bell and Thomas, {E Louise} and Gary Frost and Henrik Thomsen and Elizaveta Hansen and Hansen, {Tue Haldor} and Henrik Vestergaard and Mirthe Muilwijk and Blom, {Marieke T} and {'t Hart}, {Leen M} and Francois Pattou and Violeta Raverdy and Soren Brage and Martin Ridderstr{\aa}le and Oluf Pedersen and Torben Hansen and Karina Banasik and Simon Rasmussen and S{\o}ren Brunak and {IMI-DIRECT consortium}",
note = "{\textcopyright} 2023. The Author(s). Author Correction: In the version of this article initially published, Cristina Leal Rodr{\'i}guez (Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark) was omitted from the author list. The error has been corrected in the HTML and PDF versions of the article. https://www.nature.com/articles/s41587-023-01805-9",
year = "2023",
doi = "10.1038/s41587-022-01520-x",
language = "English",
volume = "41",
pages = "399–408",
journal = "Nature Biotechnology",
issn = "1087-0156",
publisher = "nature publishing group",
number = "3",

}

RIS

TY - JOUR

T1 - Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models

T2 - [with Author Correction]

AU - Allesøe, Rosa Lundbye

AU - Lundgaard, Agnete Troen

AU - Hernández Medina, Ricardo

AU - Aguayo-Orozco, Alejandro

AU - Johansen, Joachim

AU - Nissen, Jakob Nybo

AU - Brorsson, Caroline

AU - Mazzoni, Gianluca

AU - Niu, Lili

AU - Biel, Jorge Hernansanz

AU - Brasas, Valentas

AU - Webel, Henry

AU - Benros, Michael Eriksen

AU - Pedersen, Anders Gorm

AU - Chmura, Piotr Jaroslaw

AU - Jacobsen, Ulrik Plesner

AU - Mari, Andrea

AU - Koivula, Robert

AU - Mahajan, Anubha

AU - Vinuela, Ana

AU - Tajes, Juan Fernandez

AU - Sharma, Sapna

AU - Haid, Mark

AU - Hong, Mun-Gwan

AU - Musholt, Petra B

AU - De Masi, Federico

AU - Vogt, Josef

AU - Pedersen, Helle Krogh

AU - Gudmundsdottir, Valborg

AU - Jones, Angus

AU - Kennedy, Gwen

AU - Bell, Jimmy

AU - Thomas, E Louise

AU - Frost, Gary

AU - Thomsen, Henrik

AU - Hansen, Elizaveta

AU - Hansen, Tue Haldor

AU - Vestergaard, Henrik

AU - Muilwijk, Mirthe

AU - Blom, Marieke T

AU - 't Hart, Leen M

AU - Pattou, Francois

AU - Raverdy, Violeta

AU - Brage, Soren

AU - Ridderstråle, Martin

AU - Pedersen, Oluf

AU - Hansen, Torben

AU - Banasik, Karina

AU - Rasmussen, Simon

AU - Brunak, Søren

AU - IMI-DIRECT consortium

N1 - © 2023. The Author(s). Author Correction: In the version of this article initially published, Cristina Leal Rodríguez (Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark) was omitted from the author list. The error has been corrected in the HTML and PDF versions of the article. https://www.nature.com/articles/s41587-023-01805-9

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

U2 - 10.1038/s41587-022-01520-x

DO - 10.1038/s41587-022-01520-x

M3 - Journal article

C2 - 36593394

VL - 41

SP - 399

EP - 408

JO - Nature Biotechnology

JF - Nature Biotechnology

SN - 1087-0156

IS - 3

ER -

ID: 331316397