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

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, 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, 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, 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, Ruetten, Hartmut, Pedersen, Oluf, Hansen, Torben, Dermitzakis, Emmanouil, Franks, Paul W, Schwenk, Jochen M, Adamski, Jerzy, McCarthy, Mark I, Pearson, Ewan, Banasik, Karina, Rasmussen, Simon, Brunak, Søren, IMI DIRECT Consortium, Lundgaard, Agnete Troen [0000-0001-7447-6560], Hernández Medina, Ricardo [0000-0001-6373-2362], Johansen, Joachim [0000-0001-7052-1870], Niu, Lili [0000-0003-4571-4368], Biel, Jorge Hernansanz [0000-0002-3125-2951], Leal Rodríguez, Cristina [0000-0002-3133-0630], Benros, Michael Eriksen [0000-0003-4939-9465], Pedersen, Anders Gorm [0000-0001-9650-8965], Jacobsen, Ulrik Plesner [0000-0001-9181-6854], Koivula, Robert [0000-0002-1646-4163], Vinuela, Ana [0000-0003-3771-8537], Haid, Mark [0000-0001-6118-1333], Hong, Mun-Gwan [0000-0001-8603-8293], Kennedy, Gwen [0000-0002-9856-3236], Thomas, E Louise [0000-0003-4235-4694], Frost, Gary [0000-0003-0529-6325], Hansen, Tue Haldor [0000-0001-5948-8993], Kaye, Jane [0000-0002-7311-4725], Hattersley, Andrew [0000-0001-5620-473X], Ridderstråle, Martin [0000-0002-3270-9167], Pedersen, Oluf [0000-0002-3321-3972], Hansen, Torben [0000-0001-8748-3831], Schwenk, Jochen M [0000-0001-8141-8449], Rasmussen, Simon [0000-0001-6323-9041], Brunak, Søren [0000-0003-0316-5866], and Apollo - University of Cambridge Repository

Subjects
Deep Learning, Diabetes Mellitus, Type 2, Humans, Algorithms
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.

Published
2023

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

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, 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, Ruetten, Hartmut, Pedersen, Oluf, Hansen, Torben, Dermitzakis, Emmanouil, Franks, Paul W., Schwenk, Jochen M., Adamski, Jerzy, McCarthy, Mark I., Pearson, Ewan, Banasik, Karina, Rasmussen, Simon, Brunak, S. ren, Froguel, Philippe, Thomas, Cecilia Engel, Haussler, Ragna, Beulens, Joline, Rutters, Femke, Nijpels, Giel, van Oort, Sabine, Groeneveld, Lenka, Elders, Petra, Giorgino, Toni, Rodriquez, Marianne, Nice, Rachel, Perry, Mandy, Bianzano, Susanna, Graefe-Mody, Ulrike, Hennige, Anita, Grempler, Rolf, Baum, Patrick, Stærfeldt, Hans-Henrik, Shah, Nisha, Teare, Harriet, Ehrhardt, Beate, Tillner, Joachim, Dings, Christiane, Lehr, Thorsten, Scherer, Nina, Sihinevich, Iryna, Cabrelli, Louise, Loftus, Heather, Bizzotto, Roberto, Tura, Andrea, Dekkers, Koen, van Leeuwen, Nienke, Groop, Leif, Slieker, Roderick, Ramisch, Anna, Jennison, Christopher, McVittie, Ian, Frau, Francesca, Steckel-Hamann, Birgit, Adragni, Kofi, Thomas, Melissa, Pasdar, Naeimeh Atabaki, Fitipaldi, Hugo, Kurbasic, Azra, Mutie, Pascal, Pomares-Millan, Hugo, Bonnefond, Amelie, Canouil, Mickael, Caiazzo, Robert, Verkindt, Helene, Holl, Reinhard, Kuulasmaa, Teemu, Deshmukh, Harshal, Cederberg, Henna, Laakso, Markku, Vangipurapu, Jagadish, Dale, Matilda, Thorand, Barbara, Nicolay, Claudia, Fritsche, Andreas, Hill, Anita, Hudson, Michelle, Thorne, Claire, Allin, Kristine, Arumugam, Manimozhiyan, Jonsson, Anna, Engelbrechtsen, Line, Forman, Annemette, Dutta, Avirup, Sondertoft, Nadja, Fan, Yong, Gough, Stephen, Robertson, Neil, McRobert, Nicky, Wesolowska-Andersen, Agata, Brown, Andrew, Davtian, David, Dawed, Adem, Donnelly, Louise, Palmer, Colin, White, Margaret, Ferrer, Jorge, Whitcher, Brandon, Artati, Anna, Prehn, Cornelia, Adam, Jonathan, Grallert, Harald, Gupta, Ramneek, Sackett, Peter Wad, Nilsson, Birgitte, Tsirigos, Konstantinos, Eriksen, Rebeca, Jablonka, Bernd, Uhlen, Mathias, Gassenhuber, Johann, Baltauss, Tania, de Preville, Nathalie, Klintenberg, Maria, Abdalla, Moustafa, Lundgaard, Agnete Troen [0000-0001-7447-6560], Hernández Medina, Ricardo [0000-0001-6373-2362], Johansen, Joachim [0000-0001-7052-1870], Niu, Lili [0000-0003-4571-4368], Biel, Jorge Hernansanz [0000-0002-3125-2951], Benros, Michael Eriksen [0000-0003-4939-9465], Pedersen, Anders Gorm [0000-0001-9650-8965], Jacobsen, Ulrik Plesner [0000-0001-9181-6854], Koivula, Robert [0000-0002-1646-4163], Vinuela, Ana [0000-0003-3771-8537], Haid, Mark [0000-0001-6118-1333], Hong, Mun-Gwan [0000-0001-8603-8293], Kennedy, Gwen [0000-0002-9856-3236], Thomas, E Louise [0000-0003-4235-4694], Frost, Gary [0000-0003-0529-6325], Hansen, Tue Haldor [0000-0001-5948-8993], Kaye, Jane [0000-0002-7311-4725], Hattersley, Andrew [0000-0001-5620-473X], Ridderstråle, Martin [0000-0002-3270-9167], Pedersen, Oluf [0000-0002-3321-3972], Hansen, Torben [0000-0001-8748-3831], Schwenk, Jochen M [0000-0001-8141-8449], Rasmussen, Simon [0000-0001-6323-9041], Brunak, Søren [0000-0003-0316-5866], Apollo - University of Cambridge Repository, Epidemiology and Data Science, ACS - Diabetes & metabolism, APH - Health Behaviors & Chronic Diseases, General practice, ACS - Heart failure & arrhythmias, APH - Aging & Later Life, Graduate School, and APH - Methodology

Subjects
Biomedical Engineering, Type 2 diabetes, Bioengineering, Applied Microbiology and Biotechnology, Deep Learning, SDG 3 - Good Health and Well-being, Diabetes Mellitus, Type 2, Machine learning, Molecular Medicine, Humans, Data integration, IMI DIRECT Consortium, Systems biology, Algorithms, Biotechnology
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.

Published
2023
Full Text
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3. Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts

Author

Atabaki-Pasdar, N, Ohlsson, M, Vinuela, A, Frau, F, Pomares-Millan, H, Haid, M, Jones, AG, Thomas, EL, Koivula, RW, Kurbasic, A, Mutie, PM, Fitipaldi, H, Fernandez, J, Dawed, AY, Giordano, GN, Forgie, IM, McDonald, TJ, Rutters, F, Cederberg, H, Chabanova, E, Dale, M, Masi, FD, Thomas, CE, Allin, KH, Hansen, TH, Heggie, A, Hong, M-G, Elders, PJM, Kennedy, G, Kokkola, T, Pedersen, HK, Mahajan, A, McEvoy, D, Pattou, F, Raverdy, V, Haussler, RS, Sharma, S, Thomsen, HS, Vangipurapu, J, Vestergaard, H, 't Hart, LM, Adamski, J, Musholt, PB, Brage, S, Brunak, S, Dermitzakis, E, Frost, G, Hansen, T, Laakso, M, Pedersen, O, Ridderstrale, M, Ruetten, H, Hattersley, AT, Walker, M, Beulens, JWJ, Mari, A, Schwenk, JM, Gupta, R, McCarthy, MI, Pearson, ER, Bell, JD, Pavo, I, Franks, PW, Epidemiology and Data Science, General practice, APH - Health Behaviors & Chronic Diseases, Amsterdam Reproduction & Development (AR&D), ACS - Diabetes & metabolism, ACS - Heart failure & arrhythmias, APH - Aging & Later Life, Atabaki-Pasdar, Naeimeh [0000-0001-7229-1888], Ohlsson, Mattias [0000-0003-1145-4297], Viñuela, Ana [0000-0003-3771-8537], Pomares-Millan, Hugo [0000-0001-9245-4576], Haid, Mark [0000-0001-6118-1333], Jones, Angus G. [0000-0002-0883-7599], Thomas, E. Louise [0000-0003-4235-4694], Koivula, Robert W. [0000-0002-1646-4163], Kurbasic, Azra [0000-0002-1910-2619], Fitipaldi, Hugo [0000-0001-5352-2134], Dawed, Adem Y. [0000-0003-0224-2428], Forgie, Ian M. [0000-0002-8800-6145], Cederberg, Henna [0000-0003-2901-9373], Dale, Matilda [0000-0002-5788-7744], Masi, Federico De [0000-0003-4859-4170], Thomas, Cecilia Engel [0000-0001-6201-6380], Allin, Kristine H. [0000-0002-6880-5759], Hansen, Tue H. [0000-0001-5948-8993], Elders, Petra J. M. [0000-0002-5907-7219], Kennedy, Gwen [0000-0002-9856-3236], Kokkola, Tarja [0000-0002-3303-3912], Pedersen, Helle Krogh [0000-0001-9609-7377], Mahajan, Anubha [0000-0001-5585-3420], McEvoy, Donna [0000-0003-1546-5567], Häussler, Ragna S. [0000-0003-1664-8875], Vangipurapu, Jagadish [0000-0001-6657-2659], Vestergaard, Henrik [0000-0003-3090-269X], ‘t Hart, Leen M. [0000-0003-4401-2938], Brage, Soren [0000-0002-1265-7355], Frost, Gary [0000-0003-0529-6325], Hansen, Torben [0000-0001-8748-3831], Hattersley, Andrew T. [0000-0001-5620-473X], Mari, Andrea [0000-0002-1436-5591], Schwenk, Jochen M. [0000-0001-8141-8449], Gupta, Ramneek [0000-0001-6841-6676], McCarthy, Mark I. [0000-0002-4393-0510], Pearson, Ewan R. [0000-0001-9237-8585], Bell, Jimmy D. [0000-0003-3804-1281], Franks, Paul W. [0000-0002-0520-7604], Apollo - University of Cambridge Repository, HUS Abdominal Center, Clinicum, Department of Medicine, Endokrinologian yksikkö, Jones, Angus G [0000-0002-0883-7599], Thomas, E Louise [0000-0003-4235-4694], Koivula, Robert W [0000-0002-1646-4163], Dawed, Adem Y [0000-0003-0224-2428], Forgie, Ian M [0000-0002-8800-6145], Allin, Kristine H [0000-0002-6880-5759], Hansen, Tue H [0000-0001-5948-8993], Elders, Petra JM [0000-0002-5907-7219], Häussler, Ragna S [0000-0003-1664-8875], 't Hart, Leen M [0000-0003-4401-2938], Hattersley, Andrew T [0000-0001-5620-473X], Schwenk, Jochen M [0000-0001-8141-8449], McCarthy, Mark I [0000-0002-4393-0510], Pearson, Ewan R [0000-0001-9237-8585], Bell, Jimmy D [0000-0003-3804-1281], Franks, Paul W [0000-0002-0520-7604], and IMI

Subjects
Male, Proteomics, Oral Glucose Suppression Test, Biochemistry, Machine Learning, Fats, Database and Informatics Methods, Endocrinology, Medicine and Health Sciences, Insulin, Prospective Studies, 11 Medical and Health Sciences, GLOBAL EPIDEMIOLOGY, INSULIN SENSITIVITY, Proteomic Databases, Liver Diseases, Middle Aged, Lipids, Medicine, Female, Life Sciences & Biomedicine, Research Article, Computer and Information Sciences, Endocrine Disorders, BIOMARKERS, Gastroenterology and Hepatology, Research and Analysis Methods, Risk Assessment, Diabetes Complications, Medicine, General & Internal, SDG 3 - Good Health and Well-being, Artificial Intelligence, General & Internal Medicine, NAFLD, Diabetes Mellitus, Humans, Metabolomics, Diabetic Endocrinology, Pharmacology, Science & Technology, Models, Statistical, Reproducibility of Results, Biology and Life Sciences, ALCOHOLIC STEATOHEPATITIS, Hormones, Pharmacologic-Based Diagnostics, Fatty Liver, Metabolism, Biological Databases, 3121 General medicine, internal medicine and other clinical medicine, Metabolic Disorders
Abstract

Background Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and, ultimately, hepatocellular carcinomas. We sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning. Methods and findings We utilized the baseline data from IMI DIRECT, a multicenter prospective cohort study of 3,029 European-ancestry adults recently diagnosed with T2D (n = 795) or at high risk of developing the disease (n = 2,234). Multi-omics (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI-image-derived liver fat content (, In a modelling study, Naeimeh Atabaki-Pasdar and colleagues apply machine learning techniques to develop models to predict non-alcoholic fatty liver disease diagnosis using multi-omic and clinical data from individuals with and without type 2 diabetes in the IMI DIRECT cohorts., Author summary Why was this study done? Globally, about 1 in 4 adults have non-alcoholic fatty liver disease (NAFLD), which adversely affects energy homeostasis (in particular blood glucose concentrations), blood detoxification, drug metabolism, and food digestion. Although numerous noninvasive tests to detect NAFLD exist, these typically include inaccurate blood-marker tests or expensive imaging methods. The purpose of this work was to develop accurate noninvasive methods to aid in the clinical prediction of NAFLD. What did the researchers do and find? The analyses applied machine learning methods to data from the deep-phenotyped IMI DIRECT cohorts (n = 1,514) to identify sets of highly informative variables for the prediction of NAFLD. The criterion measure was liver fat quantified from MRI. We developed a total of 18 prediction models that ranged from very inexpensive models of modest accuracy to more expensive biochemistry- and/or omics-based models with high accuracy. We found that models using measures commonly collected in either clinical settings or research studies proved adequate for the prediction of NAFLD. The addition of detailed omics data significantly improved the predictive utility of these models. We also found that of all omics markers, proteomic markers yielded the highest predictive accuracy when appropriately combined. What do these findings mean? We envisage that these new approaches to predicting fatty liver may be of clinical value when screening at-risk populations for NAFLD. The identification of specific molecular features that underlie the development of NAFLD provides novel insights into the disease’s etiology, which may lead to the development of new treatments.

Published
2020
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