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2. Swap-Free Fat-Water Separation in Dixon MRI using Conditional Generative Adversarial Networks

Author

Basty, Nicolas, Thanaj, Marjola, Cule, Madeleine, Sorokin, Elena P., Liu, Yi, Bell, Jimmy D., Thomas, E. Louise, and Whitcher, Brandon

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Dixon MRI is widely used for body composition studies. Current processing methods associated with large whole-body volumes are time intensive and prone to artifacts during fat-water separation performed on the scanner, making the data difficult to analyse. The most common artifact are fat-water swaps, where the labels are inverted at the voxel level. It is common for researchers to discard swapped data (generally around 10%), which can be wasteful and lead to unintended biases. The UK Biobank is acquiring Dixon MRI for over 100,000 participants, and thousands of swaps will occur. If those go undetected, errors will propagate into processes such as abdominal organ segmentation and dilute the results in population-based analyses. There is a clear need for a fast and robust method to accurately separate fat and water channels. In this work we propose such a method based on style transfer using a conditional generative adversarial network. We also introduce a new Dixon loss function for the generator model. Using data from the UK Biobank Dixon MRI, our model is able to predict highly accurate fat and water channels that are free from artifacts. We show that the model separates fat and water channels using either single input (in-phase) or dual input (in-phase and opposed-phase), with the latter producing improved results. Our proposed method enables faster and more accurate downstream analysis of body composition from Dixon MRI in population studies by eliminating the need for visual inspection or discarding data due to fat-water swaps.

Published
2021
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6. 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, and Brunak, Søren

Published
2023
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7. Large-Scale Analysis of Iliopsoas Muscle Volumes in the UK Biobank

Author

Fitzpatrick, Julie, Basty, Nicolas, Cule, Madeleine, Liu, Yi, Bell, Jimmy D., Thomas, E. Louise, and Whitcher, Brandon

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Quantitative Biology - Quantitative Methods
Abstract

Psoas muscle measurements are frequently used as markers of sarcopenia and predictors of health. Manually measured cross-sectional areas are most commonly used, but there is a lack of consistency regarding the position of the measurementand manual annotations are not practical for large population studies. We have developed a fully automated method to measure iliopsoas muscle volume (comprised of the psoas and iliacus muscles) using a convolutional neural network. Magnetic resonance images were obtained from the UK Biobank for 5,000 male and female participants, balanced for age, gender and BMI. Ninety manual annotations were available for model training and validation. The model showed excellent performance against out-of-sample data (dice score coefficient of 0.912 +/- 0.018). Iliopsoas muscle volumes were successfully measured in all 5,000 participants. Iliopsoas volume was greater in male compared with female subjects. There was a small but significant asymmetry between left and right iliopsoas muscle volumes. We also found that iliopsoas volume was significantly related to height, BMI and age, and that there was an acceleration in muscle volume decrease in men with age. Our method provides a robust technique for measuring iliopsoas muscle volume that can be applied to large cohorts., Comment: Julie Fitzpatrick and Nicolas Basty are joint first authors

Published
2020
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8. Image Processing and Quality Control for Abdominal Magnetic Resonance Imaging in the UK Biobank

Author

Basty, Nicolas, Liu, Yi, Cule, Madeleine, Thomas, E. Louise, Bell, Jimmy D., and Whitcher, Brandon

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Quantitative Biology - Quantitative Methods
Abstract

An end-to-end image analysis pipeline is presented for the abdominal MRI protocol used in the UK Biobank on the first 38,971 participants. Emphasis is on the processing steps necessary to ensure a high-level of data quality and consistency is produced in order to prepare the datasets for downstream quantitative analysis, such as segmentation and parameter estimation. Quality control procedures have been incorporated to detect and, where possible, correct issues in the raw data. Detection of fat-water swaps in the Dixon series is performed by a deep learning model and corrected automatically. Bone joints are predicted using a hybrid atlas-based registration and deep learning model for the shoulders, hips and knees. Simultaneous estimation of proton density fat fraction and transverse relaxivity (R2*) is performed using both the magnitude and phase information for the single-slice multiecho series. Approximately 98.1% of the two-point Dixon acquisitions were successfully processed and passed quality control, with 99.98% of the high-resolution T1-weighted 3D volumes succeeding. Approximately 99.98% of the single-slice multiecho acquisitions covering the liver were successfully processed and passed quality control, with 97.6% of the single-slice multiecho acquisitions covering the pancreas succeeding. At least one fat-water swap was detected in 1.8% of participants. With respect to the bone joints, approximately 3.3% of participants were missing at least one knee joint and 0.8% were missing at least one shoulder joint. For the participants who received both single-slice multiecho acquisition protocols for the liver a systematic difference between the two protocols was identified and modeled using multiple linear regression. The findings presented here will be invaluable for scientists who seek to use image-derived phenotypes from the abdominal MRI protocol., Comment: Fixed 2 references

Published
2020

10. Author Correction: 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, and Brunak, Søren

Published
2023
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16. MRI assessment of adipose tissue fatty acid composition in the UK Biobank and its association with diet and disease.

Author

Thanaj, Marjola, Basty, Nicolas, Whitcher, Brandon, Bell, Jimmy D., and Thomas, E. Louise

Subjects
ADIPOSE tissues, MAGNETIC resonance imaging, VEGANISM, DIET in disease, BODY size
Abstract

Objective: This study aimed to assess the fatty acid (FA) composition of abdominal subcutaneous and visceral adipose tissue (ASAT and VAT, respectively) in the UK Biobank imaging cohort (N = 33,823) using magnetic resonance imaging (MRI). Methods: We measured the fractions of saturated, monounsaturated, and polyunsaturated FA (fSFA, fMUFA, and fPUFA, respectively) in ASAT and VAT from multiecho MRI scans. We selected a subcohort of participants who followed a vegan and an omnivore diet (N = 36) to validate the effect of diet on adipose tissue. In the wider imaging cohort, we examined the relationships between adipose tissue FA composition and various traits related to disease and body size. Results: We measured adipose tissue FA composition for over 33,000 participants, revealing higher fSFA and fPUFA and lower fMUFA in VAT (p < 0.00016). fMUFA and fPUFA were higher in ASAT and lower in VAT for women (p < 0.00016). Vegan participants exhibited lower fSFA in both ASAT and VAT (p < 0.00016). VAT fSFA and fMUFA showed significant associations with disease, as well as anthropometric variables. Conclusions: This extensive analysis revealed the relationships between adipose tissue FA composition and a range of factors in a diverse population, highlighting the importance of studying body adipose tissue beyond its quantity. [ABSTRACT FROM AUTHOR]

Published
2024
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17. The Association of Cardiometabolic, Diet and Lifestyle Parameters With Plasma Glucagon-like Peptide-1: An IMI DIRECT Study.

Author

Eriksen, Rebeca, White, Margaret C, Dawed, Adem Y, Perez, Isabel Garcia, Posma, Joram M, Haid, Mark, Sharma, Sapna, Prehn, Cornelia, Thomas, E Louise, Koivula, Robert W, Bizzotto, Roberto, Mari, Andrea, Giordano, Giuseppe N, Pavo, Imre, Schwenk, Jochen M, Masi, Federico De, Tsirigos, Konstantinos D, Brunak, Søren, Viñuela, Ana, and Mahajan, Anubha

Subjects
TYPE 2 diabetes, PEOPLE with diabetes, MULTIPLE regression analysis, INSULIN resistance, FOOD consumption
Abstract

Context The role of glucagon-like peptide-1 (GLP-1) in type 2 diabetes (T2D) and obesity is not fully understood. Objective We investigate the association of cardiometabolic, diet, and lifestyle parameters on fasting and postprandial GLP-1 in people at risk of, or living with, T2D. Methods We analyzed cross-sectional data from the two Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) cohorts, cohort 1 (n = 2127) individuals at risk of diabetes; cohort 2 (n = 789) individuals with new-onset T2D. Results Our multiple regression analysis reveals that fasting total GLP-1 is associated with an insulin-resistant phenotype and observe a strong independent relationship with male sex, increased adiposity, and liver fat, particularly in the prediabetes population. In contrast, we showed that incremental GLP-1 decreases with worsening glycemia, higher adiposity, liver fat, male sex, and reduced insulin sensitivity in the prediabetes cohort. Higher fasting total GLP-1 was associated with a low intake of wholegrain, fruit, and vegetables in people with prediabetes, and with a high intake of red meat and alcohol in people with diabetes. Conclusion These studies provide novel insights into the association between fasting and incremental GLP-1, metabolic traits of diabetes and obesity, and dietary intake, and raise intriguing questions regarding the relevance of fasting GLP-1 in the pathophysiology T2D. [ABSTRACT FROM AUTHOR]

Published
2024
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19. Genetic evidence for distinct biological mechanisms that link adiposity to type 2 diabetes: towards precision medicine

Author

Abraham, Angela, primary, Cule, Madeleine, additional, Thanaj, Marjola, additional, Basty, Nicolas, additional, Hashemloo, M. Amin, additional, Sorokin, Elena P., additional, Whitcher, Brandon, additional, Burgess, Stephen, additional, Bell, Jimmy D., additional, Sattar, Naveed, additional, Thomas, E. Louise, additional, and Yaghootkar, Hanieh, additional

Published
2024
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20. Ultra weak photon emission—a brief review

Author

Mould, Rhys R., primary, Mackenzie, Alasdair M., additional, Kalampouka, Ifigeneia, additional, Nunn, Alistair V. W., additional, Thomas, E. Louise, additional, Bell, Jimmy D., additional, and Botchway, Stanley W., additional

Published
2024
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22. Machine Learning Classification of Females Susceptibility to Visceral Fat Associated Diseases

Author

Aldraimli, Mahmoud, Soria, Daniele, Parkinson, James, Whitcher, Brandon, Thomas, E. Louise, Bell, Jimmy D., Chaussalet, Thierry J., Dwek, Miriam V., Magjarevic, Ratko, Series Editor, Ładyżyński, Piotr, Associate Editor, Ibrahim, Fatimah, Associate Editor, Lackovic, Igor, Associate Editor, Rock, Emilio Sacristan, Associate Editor, Henriques, Jorge, editor, Neves, Nuno, editor, and de Carvalho, Paulo, editor

Published
2020
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23. Advancing Pancreas Segmentation in Multi-protocol MRI Volumes Using Hausdorff-Sine Loss Function

Author

Asaturyan, Hykoush, Thomas, E. Louise, Fitzpatrick, Julie, Bell, Jimmy D., Villarini, Barbara, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Suk, Heung-Il, editor, Liu, Mingxia, editor, Yan, Pingkun, editor, and Lian, Chunfeng, editor

Published
2019
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25. Genetic studies of abdominal MRI data identify genes regulating hepcidin as major determinants of liver iron concentration

Author

Jennison, Christopher, Ehrhardt, Beate, Baum, Patrick, Schoelsch, Corinna, Freijer, Jan, Grempler, Rolf, Graefe-Mody, Ulrike, Hennige, Anita, Dings, Christiane, Lehr, Thorsten, Scherer, Nina, Sihinecich, Iryna, Pattou, Francois, Raverdi, Violeta, Caiazzo, Robert, Torres, Fanelly, Verkindt, Helene, Mari, Andrea, Tura, Andrea, Giorgino, Toni, Bizzotto, Roberto, Froguel, Philippe, Bonneford, Amelie, Canouil, Mickael, Dhennin, Veronique, Brorsson, Caroline, Brunak, Soren, De Masi, Federico, Gudmundsdóttir, Valborg, Pedersen, Helle, Banasik, Karina, Thomas, Cecilia, Sackett, Peter, Staerfeldt, Hans-Henrik, Lundgaard, Agnete, Nilsson, Birgitte, Nielsen, Agnes, Mazzoni, Gianluca, Karaderi, Tugce, Rasmussen, Simon, Johansen, Joachim, Allesøe, Rosa, Fritsche, Andreas, Thorand, Barbara, Adamski, Jurek, Grallert, Harald, Haid, Mark, Sharma, Sapna, Troll, Martina, Adam, Jonathan, Ferrer, Jorge, Eriksen, Heather, Frost, Gary, Haussler, Ragna, Hong, Mun-gwan, Schwenk, Jochen, Uhlen, Mathias, Nicolay, Claudia, Pavo, Imre, Steckel-Hamann, Birgit, Thomas, Melissa, Adragni, Kofi, Wu, Han, Hart, Leen't, Roderick, Slieker, van Leeuwen, Nienke, Dekkers, Koen, Frau, Francesca, Gassenhuber, Johann, Jablonka, Bernd, Musholt, Petra, Ruetten, Hartmut, Tillner, Joachim, Baltauss, Tania, Bernard Poenaru, Oana, de Preville, Nathalie, Rodriquez, Marianne, Arumugam, Manimozhiyan, Allin, Kristine, Engelbrechtsen, Line, Hansen, Torben, Hansen, Tue, Forman, Annemette, Jonsson, Anna, Pedersen, Oluf, Dutta, Avirup, Vogt, Josef, Vestergaard, Henrik, Laakso, Markku, Kokkola, Tarja, Kuulasmaa, Teemu, Franks, Paul, Giordano, Nick, Pomares-Millan, Hugo, Fitipaldi, Hugo, Mutie, Pascal, Klintenberg, Maria, Bergstrom, Margit, Groop, Leif, Ridderstrale, Martin, Atabaki Pasdar, Naeimeh, Deshmukh, Harshal, Heggie, Alison, Wake, Dianne, McEvoy, Donna, McVittie, Ian, Walker, Mark, Hattersley, Andrew, Hill, Anita, Jones, Angus, McDonald, Timothy, Perry, Mandy, Nice, Rachel, Hudson, Michelle, Thorne, Claire, Dermitzakis, Emmanouil, Viñuela, Ana, Cabrelli, Louise, Loftus, Heather, Dawed, Adem, Donnelly, Louise, Forgie, Ian, Pearson, Ewan, Palmer, Colin, Brown, Andrew, Koivula, Robert, Wesolowska-Andersen, Agata, Abdalla, Moustafa, McRobert, Nicky, Fernandez, Juan, Jiao, Yunlong, Robertson, Neil, Gough, Stephen, Kaye, Jane, Mourby, Miranda, Mahajan, Anubha, McCarthy, Mark, Shah, Nisha, Teare, Harriet, Holl, Reinhard, Koopman, Anitra, Rutters, Femke, Beulens, Joline, Groeneveld, Lenka, Bell, Jimmy, Thomas, Louise, Whitcher, Brandon, Wilman, Henry R., Parisinos, Constantinos A., Atabaki-Pasdar, Naeimeh, Kelly, Matt, Thomas, E. Louise, Neubauer, Stefan, Hingorani, Aroon D., Patel, Riyaz S., Hemingway, Harry, Franks, Paul W., Bell, Jimmy D., Banerjee, Rajarshi, and Yaghootkar, Hanieh

Published
2019
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28. LEAP2 changes with body mass and food intake in humans and mice

Author

Mani, Bharath K., Puzziferri, Nancy, He, Zhenyan, Rodriguez, Juan A., Osborne- Lawrence, Sherri, Metzger, Nathan P., Chhina, Navpreet, Gaylinn, Bruce, Thorner, Michael O., Thomas, E. Louise, Bell, Jimmy D., Williams, Kevin W., Goldstone, Anthony P., and Zigman, Jeffrey M.

Subjects
Obesity, Neuropeptide Y, Bariatric surgery, Type 2 diabetes, Fasting, Body weight, Blood glucose, Type 1 diabetes, Neurons, Surgery, Diabetes mellitus, Phenotypes, Peptides, Advertising executives, Glucose, Gastric bypass, Health care industry, University of London. Imperial College of Science and Technology
Abstract

Acyl-ghrelin administration increases food intake, body weight, and blood glucose. In contrast, mice lacking ghrelin or ghrelin receptors (GHSRs) exhibit life-threatening hypoglycemia during starvation-like conditions, but do not consistently exhibit overt metabolic phenotypes when given ad libitum food access. These results, and findings of ghrelin resistance in obese states, imply nutritional state dependence of ghrelin's metabolic actions. Here, we hypothesized that liver-enriched antimicrobial peptide-2 (LEAP2), a recently characterized endogenous GHSR antagonist, blunts ghrelin action during obese states and postprandially. To test this hypothesis, we determined changes in plasma LEAP2 and acyl-ghrelin due to fasting, eating, obesity, Roux-en-Y gastric bypass (RYGB), vertical sleeve gastrectomy (VSG), oral glucose administration, and type 1 diabetes mellitus (T1DM) using humans and/or mice. Our results suggest that plasma LEAP2 is regulated by metabolic status: its levels increased with body mass and blood glucose and decreased with fasting, RYGB, and in postprandial states following VSG. These changes were mostly opposite of those of acyl-ghrelin. Furthermore, using electrophysiology, we showed that LEAP2 both hyperpolarizes and prevents acyl-ghrelin from activating arcuate NPY neurons. We predict that the plasma LEAP2/acyl-ghrelin molar ratio may be a key determinant modulating acyl-ghrelin activity in response to body mass, feeding status, and blood glucose., Introduction Ghrelin is a mainly stomach-derived hormone that helps the body respond to changes in metabolic state by engaging growth hormone secretagogue receptors (GHSRs, also known as ghrelin receptors) expressed [...]

Published
2019
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29. Discovery of biomarkers for glycaemic deterioration before and after the onset of type 2 diabetes: descriptive characteristics of the epidemiological studies within the IMI DIRECT Consortium

Author

Koivula, Robert W., Forgie, Ian M., Kurbasic, Azra, Viñuela, Ana, Heggie, Alison, Giordano, Giuseppe N., Hansen, Tue H., Hudson, Michelle, Koopman, Anitra D. M., Rutters, Femke, Siloaho, Maritta, Allin, Kristine H., Brage, Søren, Brorsson, Caroline A., Dawed, Adem Y., De Masi, Federico, Groves, Christopher J., Kokkola, Tarja, Mahajan, Anubha, Perry, Mandy H., Rauh, Simone P., Ridderstråle, Martin, Teare, Harriet J. A., Thomas, E. Louise, Tura, Andrea, Vestergaard, Henrik, White, Tom, Adamski, Jerzy, Bell, Jimmy D., Beulens, Joline W., Brunak, Søren, Dermitzakis, Emmanouil T., Froguel, Philippe, Frost, Gary, Gupta, Ramneek, Hansen, Torben, Hattersley, Andrew, Jablonka, Bernd, Kaye, Jane, Laakso, Markku, McDonald, Timothy J., Pedersen, Oluf, Schwenk, Jochen M., Pavo, Imre, Mari, Andrea, McCarthy, Mark I., Ruetten, Hartmut, Walker, Mark, Pearson, Ewan, Franks, Paul W., and for the IMI DIRECT Consortium

Published
2019
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36. Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models:[with Author Correction]

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, and Brunak, Søren

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

41. Rifaximin in non‐alcoholic steatohepatitis: An open‐label pilot study

Author

Cobbold, Jeremy F.L., Atkinson, Stephen, Marchesi, Julian R., Smith, Ann, Wai, Sann N., Stove, Julie, Shojaee‐Moradie, Fariba, Jackson, Nicola, Umpleby, A. Margot, Fitzpatrick, Julie, Thomas, E. Louise, Bell, Jimmy D., Holmes, Elaine, Taylor‐Robinson, Simon D., Goldin, Robert D., Yee, Michael S., Anstee, Quentin M., and Thursz, Mark R.

Published
2018
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45. Author response: Disease consequences of higher adiposity uncoupled from its adverse metabolic effects using Mendelian randomisation

Author

Martin, Susan, primary, Tyrrell, Jessica, additional, Thomas, E Louise, additional, Bown, Matthew J, additional, Wood, Andrew R, additional, Beaumont, Robin N, additional, Tsoi, Lam C, additional, Stuart, Philip E, additional, Elder, James T, additional, Law, Philip, additional, Houlston, Richard, additional, Kabrhel, Christopher, additional, Papadimitriou, Nikos, additional, Gunter, Marc J, additional, Bull, Caroline J, additional, Bell, Joshua A, additional, Vincent, Emma E, additional, Sattar, Naveed, additional, Dunlop, Malcolm G, additional, Tomlinson, Ian PM, additional, Bell, Jimmy D, additional, Frayling, Timothy M, additional, and Yaghootkar, Hanieh, additional

Published
2021
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47. Lipidomics and body fat composition analysis characterises specific differences in cholesterol metabolism and steatosis between hepatitis C virus genotypes 1 and 3

Author

Sheridan, David A, primary, Shawa, Isaac Thom, additional, Thomas, E. Louise, additional, Felmlee, Daniel J., additional, Bridge, Simon H., additional, Neely, Dermot, additional, Cobbold, Jeremy F., additional, Holmes, Elaine, additional, Bassendine, Margaret F., additional, and Taylor-Robinson, Simon D., additional

Published
2021
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48. Effects of targeted delivery of propionate to the human colon on appetite regulation, body weight maintenance and adiposity in overweight adults

Author

Chambers, Edward S, Viardot, Alexander, Psichas, Arianna, Morrison, Douglas J, Murphy, Kevin G, Zac-Varghese, Sagen E K, MacDougall, Kenneth, Preston, Tom, Tedford, Catriona, Finlayson, Graham S, Blundell, John E, Bell, Jimmy D, Thomas, E Louise, Mt-Isa, Shahrul, Ashby, Deborah, Gibson, Glen R, Kolida, Sofia, Dhillo, Waljit S, Bloom, Stephen R, Morley, Wayne, Clegg, Stuart, and Frost, Gary

Published
2015
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50. Inferring causal pathways between metabolic processes and liver fat accumulation: an IMI DIRECT study

Author

Atabaki-Pasdar, Naeimeh, primary, Pomares-Millan, Hugo, additional, Koivula, Robert W, additional, Tura, Andrea, additional, Brown, Andrew, additional, Viñuela, Ana, additional, Agudelo, Leandro, additional, Coral, Daniel, additional, van Oort, Sabine, additional, Allin, Kristine, additional, Chabanova, Elizaveta, additional, Cederberg, Henna, additional, De Masi, Federico, additional, Elders, Petra, additional, Tajes, Juan Fernandez, additional, Forgie, Ian M, additional, Hansen, Tue H, additional, Heggie, Alison, additional, Jones, Angus, additional, Kokkola, Tarja, additional, Mahajan, Anubha, additional, McDonald, Timothy J, additional, McEvoy, Donna, additional, Tsirigos, Konstantinos, additional, Teare, Harriet, additional, Vangipurapu, Jagadish, additional, Vestergaard, Henrik, additional, Adamski, Jerzy, additional, Beulens, Joline WJ, additional, Brunak, Søren, additional, Dermitzakis, Emmanouil, additional, Hansen, Torben, additional, Hattersley, Andrew T, additional, Laakso, Markku, additional, Pedersen, Oluf, additional, Ridderstråle, Martin, additional, Ruetten, Hartmut, additional, Rutters, Femke, additional, Schwenk, Jochen M, additional, Walker, Mark, additional, Giordano, Giuseppe N, additional, Ohlsson, Mattias, additional, Gupta, Ramneek, additional, Mari, Andrea, additional, McCarthy, Mark I, additional, Thomas, E Louise, additional, Bell, Jimmy D, additional, Pavo, Imre, additional, Pearson, Ewan R, additional, and Franks, Paul W, additional

Published
2021
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