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4. 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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6. 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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12. 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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13. 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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15. 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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16. 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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24. 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

30. 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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32. 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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34. 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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35. Disease consequences of higher adiposity uncoupled from its adverse metabolic effects using Mendelian randomisation.

Author

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

Published
2022
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36. A cross-sectional MR study of body fat volumes and distribution in chronic schizophrenia

Author

Emanuele F. Osimo, Stefan P. Brugger, E. Louise Thomas, Oliver D. Howes, Medical Research Council (MRC), Osimo, Emanuele F [0000-0001-6239-5691], Thomas, E Louise [0000-0003-4235-4694], Apollo - University of Cambridge Repository, Osimo, Emanuele F. [0000-0001-6239-5691], and Thomas, E. Louise [0000-0003-4235-4694]

Subjects
2 Aetiology, 2.3 Psychological, social and economic factors, Prevention, brief-communication, 32 Biomedical and Clinical Sciences, 3 Good Health and Well Being, Serious Mental Illness, Cardiovascular, Brief Communication, humanities, 3214 Pharmacology and Pharmaceutical Sciences, Oral and gastrointestinal, Brain Disorders, Stroke, 631/378/340, Mental Health, Clinical Research, 692/699/476/1799, Schizophrenia, Biomedical Imaging, 2.1 Biological and endogenous factors, Obesity, Metabolic and endocrine, Nutrition
Abstract

Funder: DH | National Institute for Health Research (NIHR); doi: https://doi.org/10.13039/501100000272, Funder: RCUK | Medical Research Council (MRC); doi: https://doi.org/10.13039/501100000265, People with schizophrenia show higher risk for abdominal obesity than the general population, which could contribute to excess mortality. However, it is unclear whether this is driven by alterations in abdominal fat partitioning. Here, we test the hypothesis that individuals with schizophrenia show a higher proportion of visceral to total body fat measured using magnetic resonance imaging (MRI). We recruited 38 participants with schizophrenia and 38 healthy controls matched on age, sex, ethnicity, and body mass index. We found no significant differences in body fat distribution between groups, suggesting that increased abdominal obesity in schizophrenia is not associated with altered fat distribution.

Published
2022

37. Adipose tissue dysfunction, inflammation, and insulin resistance: alternative pathways to cardiac remodelling in schizophrenia. A multimodal, case-control study

Author

Emanuele F. Osimo, Mark Sweeney, Antonio de Marvao, Alaine Berry, Ben Statton, Benjamin I. Perry, Toby Pillinger, Thomas Whitehurst, Stuart A. Cook, Declan P. O’Regan, E. Louise Thomas, Oliver D. Howes, Osimo, Emanuele F [0000-0001-6239-5691], Berry, Alaine [0000-0001-6049-4418], Statton, Ben [0000-0001-5118-7977], Perry, Benjamin I [0000-0002-1533-026X], Pillinger, Toby [0000-0002-6074-2626], Whitehurst, Thomas [0000-0002-7005-902X], Thomas, E Louise [0000-0003-4235-4694], Howes, Oliver D [0000-0002-2928-1972], Apollo - University of Cambridge Repository, Wellcome Trust, British Medical Association, and Perry, Ben [0000-0002-1533-026X]

Subjects
Male, PLASMA ADIPONECTIN LEVELS, Neurosciences. Biological psychiatry. Neuropsychiatry, Pathogenesis, behavioral disciplines and activities, 1117 Public Health and Health Services, Cellular and Molecular Neuroscience, 692/699/476/1799, MAGNETIC-RESONANCE, mental disorders, 692/53/2421, Humans, LIFE EXPECTANCY, Biological Psychiatry, FATTY LIVER-DISEASE, METABOLIC SYNDROME, 692/420, Psychiatry, HYPOADIPONECTINEMIA, Inflammation, Science & Technology, Ventricular Remodeling, MORTALITY, article, 1103 Clinical Sciences, Diagnostic markers, ASSOCIATION, Psychiatry and Mental health, Adipose Tissue, 1701 Psychology, Case-Control Studies, ENDOTHELIAL DYSFUNCTION, 59/57, Schizophrenia, Insulin Resistance, Life Sciences & Biomedicine, ANTIPSYCHOTICS, RC321-571
Abstract

Funder: DH | National Institute for Health Research (NIHR); doi: https://doi.org/10.13039/501100000272, Funder: British Medical Association (BMA); doi: https://doi.org/10.13039/501100000374, Cardiovascular diseases are the leading cause of death in schizophrenia. Patients with schizophrenia show evidence of concentric cardiac remodelling (CCR), defined as an increase in left-ventricular mass over end-diastolic volumes. CCR is a predictor of cardiac disease, but the molecular pathways leading to this in schizophrenia are unknown. We aimed to explore the relevance of hypertensive and non-hypertensive pathways to CCR and their potential molecular underpinnings in schizophrenia. In this multimodal case-control study, we collected cardiac and whole-body fat magnetic resonance imaging (MRI), clinical measures, and blood levels of several cardiometabolic biomarkers known to potentially cause CCR from individuals with schizophrenia, alongside healthy controls (HCs) matched for age, sex, ethnicity, and body surface area. Of the 50 participants, 34 (68%) were male. Participants with schizophrenia showed increases in cardiac concentricity (d = 0.71, 95% CI: 0.12, 1.30; p = 0.01), indicative of CCR, but showed no differences in overall content or regional distribution of adipose tissue compared to HCs. Despite the cardiac changes, participants with schizophrenia did not demonstrate activation of the hypertensive CCR pathway; however, they showed evidence of adipose dysfunction: adiponectin was reduced (d = -0.69, 95% CI: -1.28, -0.10; p = 0.02), with evidence of activation of downstream pathways, including hypertriglyceridemia, elevated C-reactive protein, fasting glucose, and alkaline phosphatase. In conclusion, people with schizophrenia showed adipose tissue dysfunction compared to body mass-matched HCs. The presence of non-hypertensive CCR and a dysmetabolic phenotype may contribute to excess cardiovascular risk in schizophrenia. If our results are confirmed, acting on this pathway could reduce cardiovascular risk and resultant life-years lost in people with schizophrenia.

Published
2021

38. Quantifying the effect of nutritional interventions on metabolic resilience using personalized computational models.

Author

O'Donovan SD, Rundle M, Thomas EL, Bell JD, Frost G, Jacobs DM, Wanders A, de Vries R, Mariman ECM, van Baak MA, Sterkman L, Nieuwdorp M, Groen AK, Arts ICW, van Riel NAW, and Afman LA

Abstract

The manifestation of metabolic deteriorations that accompany overweight and obesity can differ greatly between individuals, giving rise to a highly heterogeneous population. This inter-individual variation can impede both the provision and assessment of nutritional interventions as multiple aspects of metabolic health should be considered at once. Here, we apply the Mixed Meal Model, a physiology-based computational model, to characterize an individual's metabolic health in silico. A population of 342 personalized models were generated using data for individuals with overweight and obesity from three independent intervention studies, demonstrating a strong relationship between the model-derived metric of insulin resistance (ρ = 0.67, p < 0.05) and the gold-standard hyperinsulinemic-euglycemic clamp. The model is also shown to quantify liver fat accumulation and β-cell functionality. Moreover, we show that personalized Mixed Meal Models can be used to evaluate the impact of a dietary intervention on multiple aspects of metabolic health at the individual level., Competing Interests: D.M.J. and A.J.W. are employees of Unilever, which manufactures and markets consumer food products. L.S. is CEO and M.N. is in the scientific board of Caelus Pharmaceuticals, the Netherlands. However, these positions are not directly relevant for the content of this current paper. S.D.O’D., M.R., E.L.T., J.D.B., G.F., R.d.V., E.C.M.M., M.v.B., L.S., M.N., A.K.G., I.C.W.A., N.A.W.v.R., and L.A.A. declare no conflicts of interest., (© 2024 The Author(s).)

Published
2024
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39. Quantifying the contribution of triglycerides to metabolic resilience through the mixed meal model.

Author

O'Donovan SD, Erdős B, Jacobs DM, Wanders AJ, Thomas EL, Bell JD, Rundle M, Frost G, Arts ICW, Afman LA, and van Riel NAW

Abstract

Despite the pivotal role played by elevated circulating triglyceride levels in the pathophysiology of cardio-metabolic diseases many of the indices used to quantify metabolic health focus on deviations in glucose and insulin alone. We present the Mixed Meal Model, a computational model describing the systemic interplay between triglycerides, free fatty acids, glucose, and insulin. We show that the Mixed Meal Model can capture deviations in the post-meal excursions of plasma glucose, insulin, and triglyceride that are indicative of features of metabolic resilience; quantifying insulin resistance and liver fat; validated by comparison to gold-standard measures. We also demonstrate that the Mixed Meal Model is generalizable, applying it to meals with diverse macro-nutrient compositions. In this way, by coupling triglycerides to the glucose-insulin system the Mixed Meal Model provides a more holistic assessment of metabolic resilience from meal response data, quantifying pre-clinical metabolic deteriorations that drive disease development in overweight and obesity., Competing Interests: SDOD, BE, ELT, JDB, MR, GF, ICWA, LAA, and NAWvR declare no conflicts of interest. DMJ and AJW are employees of Unilever, which manufactures and markets consumer food products., (© 2022 The Authors.)

Published
2022
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