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

Rosa Lundbye Allesøe; Agnete Troen Lundgaard; Ricardo Hernández Medina; Alejandro Aguayo-Orozco; Joachim Johansen; Jakob Nybo Nissen; Caroline Brorsson; Gianluca Mazzoni; Lili Niu; Jorge Hernansanz Biel; Valentas Brasas; Henry Webel; Michael Eriksen Benros; Anders Gorm Pedersen; Piotr Jaroslaw Chmura; Ulrik Plesner Jacobsen; Andrea Mari; Robert Koivula; Anubha Mahajan; Ana Vinuela; Juan Fernandez Tajes; Sapna Sharma; Mark Haid; Mun Gwan Hong; Petra B. Musholt; Federico De Masi; Josef Vogt; Helle Krogh Pedersen; Valborg Gudmundsdottir; Angus Jones; Gwen Kennedy; Jimmy Bell; E. Louise Thomas; Gary Frost; Henrik Thomsen; Elizaveta Hansen; Tue Haldor Hansen; Henrik Vestergaard; Mirthe Muilwijk; Marieke T. Blom; Leen M. ‘t Hart; Torben Hansen; Jochen M. Schwenk; Jerzy  Adamski; Mark I. McCarthy; Ewan Pearson; Karina Banasik; Simon Rasmussen; Søren Brunak; Paul W. Franks

doi:10.1038/s41587-022-01520-x

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

Rosa Lundbye Allesøe, Agnete Troen Lundgaard, Ricardo Hernández Medina, Alejandro Aguayo-Orozco, Joachim Johansen, Jakob Nybo Nissen, Caroline Brorsson, Gianluca Mazzoni, Lili Niu, Jorge Hernansanz Biel, Valentas Brasas, Henry Webel, Michael Eriksen Benros, Anders Gorm Pedersen, Piotr Jaroslaw Chmura, Ulrik Plesner Jacobsen, Andrea Mari, Robert Koivula, Anubha Mahajan, Ana VinuelaJuan Fernandez Tajes, Sapna Sharma, Mark Haid, Mun Gwan Hong, Petra B. Musholt, Federico De Masi, Josef Vogt, Helle Krogh Pedersen, Valborg Gudmundsdottir, Angus Jones, Gwen Kennedy, Jimmy Bell, E. Louise Thomas, Gary Frost, Henrik Thomsen, Elizaveta Hansen, Tue Haldor Hansen, Henrik Vestergaard, Mirthe Muilwijk, Marieke T. Blom, Leen M. ‘t Hart, Torben Hansen, Jochen M. Schwenk, Jerzy Adamski, Mark I. McCarthy, Ewan Pearson, Karina Banasik, Simon Rasmussen^*, Søren Brunak^*, Paul W. Franks

^*Corresponding author for this work

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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.

Original language	English
Journal	Nature Biotechnology
Volume	41
Issue number	3
Pages (from-to)	399-408
ISSN	1087-0156
DOIs	https://doi.org/10.1038/s41587-022-01520-x
Publication status	Published - 2023

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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., ... Franks, P. W. (2023). Discovery of drug–omics associations in type 2 diabetes with generative deep-learning models. Nature Biotechnology, 41(3), 399-408. https://doi.org/10.1038/s41587-022-01520-x

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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 Hong, {Mun Gwan} 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 {{\textquoteleft}t Hart}, {Leen M.} and Torben Hansen and Schwenk, {Jochen M.} and Jerzy Adamski and McCarthy, {Mark I.} and Ewan Pearson and Karina Banasik and Simon Rasmussen and S{\o}ren Brunak and Franks, {Paul W.}",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s).",

year = "2023",

doi = "10.1038/s41587-022-01520-x",

language = "English",

volume = "41",

pages = "399--408",

journal = "Nature Biotechnology",

issn = "1087-0156",

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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, MG, 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, Hansen, T, Schwenk, JM, Adamski, J, McCarthy, MI, Pearson, E, Banasik, K, Rasmussen, S, Brunak, S & Franks, PW 2023, 'Discovery of drug–omics associations in type 2 diabetes with generative deep-learning models', Nature Biotechnology, vol. 41, no. 3, pp. 399-408. https://doi.org/10.1038/s41587-022-01520-x

TY - JOUR

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

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 - Hansen, Torben

AU - Schwenk, Jochen M.

AU - Adamski, Jerzy

AU - McCarthy, Mark I.

AU - Pearson, Ewan

AU - Banasik, Karina

AU - Rasmussen, Simon

AU - Brunak, Søren

AU - Franks, Paul W.

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.

UR - https://www.nature.com/articles/s41587-023-01805-9

U2 - 10.1038/s41587-022-01520-x

DO - 10.1038/s41587-022-01520-x

M3 - Journal article

C2 - 36593394

AN - SCOPUS:85145508974

SN - 1087-0156

VL - 41

SP - 399

EP - 408

JO - Nature Biotechnology

JF - Nature Biotechnology

IS - 3

ER -

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

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