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7. 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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10. 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
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13. Elucidating causal relationships between energy homeostasis and cardiometabolic outcomes

Author

Mutie, Pascal and Mutie, Pascal

Abstract

Energy metabolism dyshomeostasis is associated with multiple health problems. For example, abundant epidemiological data show that obesity and overweight increase the risk of cardiometabolic diseases and early mortality. Type 2 diabetes (T2D), characterized by chronically elevated blood glucose, is also associated with debilitating complications, high healthcare costs and mortality, with cardiovascular complications accounting for more than half of T2D-related deaths. Prediabetes, which is defined as elevated blood glucose below the diagnostic threshold for T2D, affects approximately 350M people worldwide, with about 35-50% developing T2D within 5 years. Further, non-alcoholic fatty liver disease, a form of ectopic fat deposition as a result of energy imbalance, is associated with increased risk of T2D, CVD and hepatocellular carcinoma. Determination of causal relationships between phenotypes related to positive energy balance and disease outcomes, as well as elucidation of the nature of these relationships, may help inform public health intervention policies. In addition, utilizing big data and machine learning (ML) approaches can improve prediction of outcomes related to excess adiposity both for research purposes and eventual validation and clinical translation. AimsIn paper 1, I set out to summarize observational evidence and further determine the causal relationships between prediabetes and common vascular complications associated with T2D i.e., coronary artery disease (CAD), stroke and renal disease. In paper 2, I studied the association between LRIG1 genetic variants and BMI, T2D and lipid biomarkers. In paper 3, we used ML to identify novel molecular features associated with non-alcoholic fatty liver disease (NAFLD). In paper 4, I elucidate the nature of causal relationships between BMI and cardiometabolic traits and investigate sex differences within the causal framework.ResultsPrediabetes was associated with CAD and stroke but not renal disease in observat

Published
2022

14. Subtyping of obesity and type 2 diabetes using genetic discordance: a phenome-wide comparative analysis

Author

Coral, Daniel, primary, Tajes, Juan Fernandez, additional, Tsereteli, Neli, additional, Giordano, Giuseppe, additional, Pomares-Millan, Hugo, additional, Mutie, Pascal, additional, Atabaki-Pasdar, Naeimeh, additional, Kalamajski, Sebastian, additional, Poveda, Alaitz, additional, Miller-Fleming, Tyne, additional, Zhong, Xue, additional, Cox, Nancy, additional, and Franks, Paul, additional

Published
2022
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15. Predicting and elucidating the etiology of fatty liver disease:A machine learning modeling and validation study in the IMI DIRECT cohorts

Author

Atabaki-Pasdar, Naeimeh, Ohlsson, Mattias, Viñuela, Ana, Frau, Francesca, Pomares-Millan, Hugo, Haid, Mark, Jones, Angus G, Thomas, E Louise, Koivula, Robert W, Kurbasic, Azra, Mutie, Pascal M, Fitipaldi, Hugo, Fernandez, Juan, Dawed, Adem Y, Giordano, Giuseppe N, Forgie, Ian M, McDonald, Timothy J, Rutters, Femke, Cederberg, Henna, Chabanova, Elizaveta, Dale, Matilda, Masi, Federico De, Thomas, Cecilia Engel, Allin, Kristine H., Hansen, Tue H, Heggie, Alison, Hong, Mun-Gwan, Elders, Petra J M, Kennedy, Gwen, Kokkola, Tarja, Pedersen, Helle Krogh, Mahajan, Anubha, McEvoy, Donna, Pattou, Francois, Raverdy, Violeta, Häussler, Ragna S, Sharma, Sapna, Thomsen, Henrik S, Vangipurapu, Jagadish, Vestergaard, Henrik, Adamski, Jerzy, Musholt, Petra B, Brage, Søren, Brunak, Søren, Dermitzakis, Emmanouil, Frost, Gary, Hansen, Torben, Laakso, Markku, and Pedersen, Oluf

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 (CONCLUSIONS: In this study, we developed several models with different combinations of clinical and omics data and identified biological features that appear to be associated with liver fat accumulation. In general, the clinical variables showed better prediction ability than the complex omics variables. However, the combination of omics and clinical variables yielded the highest accuracy. We have incorporated the developed clinical models into a web interface (see: https://www.predictliverfat.org/) and made it available to the community.TRIAL REGISTRATION: ClinicalTrials.gov NCT03814915.

Published
2020
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16. Additional file 1 of Different domains of self-reported physical activity and risk of type 2 diabetes in a population-based Swedish cohort: the Malmö diet and Cancer study

Author

Mutie, Pascal, Drake, Isabel, Ericson, Ulrika, Teleka, Stanley, Christina-Alexandra Schulz, Stocks, Tanja, and Sonestedt, Emily

Abstract

Additional file 1: Supplemental Table 1. List of leisure-time physical activities in the MDC cohort. Supplemental Table 2. Correlations between different measures of physical activity in the MDCS. Supplementary Table 3. HR and 95% CI for association between physical activity and T2D in the MDCS using spline-based cut-points, 1991-1996. Supplemental Table 4. Association between measures of physical activity risk of T2D among adequate reporters of energy intake in the MDCS, 1991-1996. Supplemental Table 5. Measures of physical activity and risk (HR, 95% CI) of incident T2D in the MDCS adjusted for different measures of adiposity, 1991-1996. Supplemental Table 6. Measures of physical activity and risk (HR, 95% CI) of incident T2D excluding the first 2 years of follow-up in the MDCS, 1991-1996. Supplemental Table 7. Measures of physical activity and risk (HR, 95% CI) of incident T2D, confirmed from at least two sources in the MDCS, 1991-1996. This file contains additional data relevant to interpretation of the study findings. It includes information on the different types of actual physical activities reported by participants, correlations between the different measures of physical activity and results of the various sensitivity analyses conducted

Published
2020
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17. Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts

Author

Atabaki-Pasdar, Naeimeh, primary, Ohlsson, Mattias, additional, Viñuela, Ana, additional, Frau, Francesca, additional, Pomares-Millan, Hugo, additional, Haid, Mark, additional, Jones, Angus G., additional, Thomas, E. Louise, additional, Koivula, Robert W., additional, Kurbasic, Azra, additional, Mutie, Pascal M., additional, Fitipaldi, Hugo, additional, Fernandez, Juan, additional, Dawed, Adem Y., additional, Giordano, Giuseppe N., additional, Forgie, Ian M., additional, McDonald, Timothy J., additional, Rutters, Femke, additional, Cederberg, Henna, additional, Chabanova, Elizaveta, additional, Dale, Matilda, additional, Masi, Federico De, additional, Thomas, Cecilia Engel, additional, Allin, Kristine H., additional, Hansen, Tue H., additional, Heggie, Alison, additional, Hong, Mun-Gwan, additional, Elders, Petra J. M., additional, Kennedy, Gwen, additional, Kokkola, Tarja, additional, Pedersen, Helle Krogh, additional, Mahajan, Anubha, additional, McEvoy, Donna, additional, Pattou, Francois, additional, Raverdy, Violeta, additional, Häussler, Ragna S., additional, Sharma, Sapna, additional, Thomsen, Henrik S., additional, Vangipurapu, Jagadish, additional, Vestergaard, Henrik, additional, ‘t Hart, Leen M., additional, Adamski, Jerzy, additional, Musholt, Petra B., additional, Brage, Soren, additional, Brunak, Søren, additional, Dermitzakis, Emmanouil, additional, Frost, Gary, additional, Hansen, Torben, additional, Laakso, Markku, additional, Pedersen, Oluf, additional, Ridderstråle, Martin, additional, Ruetten, Hartmut, additional, Hattersley, Andrew T., additional, Walker, Mark, additional, Beulens, Joline W. J., additional, Mari, Andrea, additional, Schwenk, Jochen M., additional, Gupta, Ramneek, additional, McCarthy, Mark I., additional, Pearson, Ewan R., additional, Bell, Jimmy D., additional, Pavo, Imre, additional, and Franks, Paul W., additional

Published
2020
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18. Predicting and elucidating the etiology of fatty liver disease using a machine learning-based approach: an IMI DIRECT study

Author

Atabaki-Pasdar, Naeimeh, primary, Ohlsson, Mattias, additional, Viñuela, Ana, additional, Frau, Francesca, additional, Pomares-Millan, Hugo, additional, Haid, Mark, additional, Jones, Angus G, additional, Thomas, E Louise, additional, Koivula, Robert W, additional, Kurbasic, Azra, additional, Mutie, Pascal M, additional, Fitipaldi, Hugo, additional, Fernandez, Juan, additional, Dawed, Adem Y, additional, Giordano, Giuseppe N, additional, Forgie, Ian M, additional, McDonald, Timothy J, additional, Rutters, Femke, additional, Cederberg, Henna, additional, Chabanova, Elizaveta, additional, Dale, Matilda, additional, De Masi, Federico, additional, Thomas, Cecilia Engel, additional, Allin, Kristine H, additional, Hansen, Tue H, additional, Heggie, Alison, additional, Hong, Mun-Gwan, additional, Elders, Petra JM, additional, Kennedy, Gwen, additional, Kokkola, Tarja, additional, Pedersen, Helle Krogh, additional, Mahajan, Anubha, additional, McEvoy, Donna, additional, Pattou, Francois, additional, Raverdy, Violeta, additional, Häussler, Ragna S, additional, Sharma, Sapna, additional, Thomsen, Henrik S, additional, Vangipurapu, Jagadish, additional, Vestergaard, Henrik, additional, ‘t Hart, Leen M, additional, Adamski, Jerzy, additional, Musholt, Petra B, additional, Brage, Soren, additional, Brunak, Søren, additional, Dermitzakis, Emmanouil, additional, Frost, Gary, additional, Hansen, Torben, additional, Laakso, Markku, additional, Pedersen, Oluf, additional, Ridderstråle, Martin, additional, Ruetten, Hartmut, additional, Hattersley, Andrew T, additional, Walker, Mark, additional, Beulens, Joline WJ, additional, Mari, Andrea, additional, Schwenk, Jochen M, additional, Gupta, Ramneek, additional, McCarthy, Mark I, additional, Pearson, Ewan R, additional, Bell, Jimmy D, additional, Pavo, Imre, additional, and Franks, Paul W, additional

Published
2020
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19. Genetic studies of abdominal MRI data identify genes regulating hepcidin as major determinants of liver iron concentration

Author

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

Published
2019
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20. Lifestyle precision medicine : the next generation in type 2 diabetes prevention?

Author

Mutie, Pascal M., Giordano, Giuseppe N., Franks, Paul W., Mutie, Pascal M., Giordano, Giuseppe N., and Franks, Paul W.

Abstract

The driving force behind the current global type 2 diabetes epidemic is insulin resistance in overweight and obese individuals. Dietary factors, physical inactivity, and sedentary behaviors are the major modifiable risk factors for obesity. Nevertheless, many overweight/obese people do not develop diabetes and lifestyle interventions focused on weight loss and diabetes prevention are often ineffective. Traditionally, chronically elevated blood glucose concentrations have been the hallmark of diabetes; however, many individuals will either remain 'prediabetic' or regress to normoglycemia. Thus, there is a growing need for innovative strategies to tackle diabetes at scale. The emergence of biomarker technologies has allowed more targeted therapeutic strategies for diabetes prevention (precision medicine), though largely confined to pharmacotherapy. Unlike most drugs, lifestyle interventions often have systemic health-enhancing effects. Thus, the pursuance of lifestyle precision medicine in diabetes seems rational. Herein, we review the literature on lifestyle interventions and diabetes prevention, describing the biological systems that can be characterized at scale in human populations, linking them to lifestyle in diabetes, and consider some of the challenges impeding the clinical translation of lifestyle precision medicine.

Published
2017
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22. LEISURE-TIME PHYSICAL ACTIVITY AND THE ONSET OF TYPE 2 DIABETES IN THE MALMÖ DIET AND CANCER STUDY COHORT

Author

Mutie, Pascal and Mutie, Pascal

Abstract

Aims/objectives: The study’s aim was to assess the association between different levels of leisure time physical activity (LTPA) and the risk of type 2 diabetes mellitus (T2DM) and assess the association between socio-economic status (SES) and distribution of LTPA. Methods: The study was based on the Malmo Diet and Cancer Study cohort that included participants aged 44-74 years at enrolment. Demographic and covariate data from the baseline questionnaire (1991-1996) and outcomes of interest (2014) linking the cohort data to various medical and population registers was used. LTPA was assessed using a standard questionnaire as a sum of products of 17 predefined individual activities (assessed as minutes spent weekly per activity in the previous year) with their intensity factors (Metabolic Equivalent or MET) and reported as MET-hours per week. Chi-square test was used to assess the proportions of different socioeconomic groups in the various LTPA categories and Cox regression utilized to model the association between LTPA and incidence of T2DM, adjusted for age, sex, body mass index (BMI), smoking, education level and occupation. Results: There were significant differences in the distribution of LTPA levels across the different socioeconomic groups. Moderate LTPA (7.5-15 MET-hours/week) was not significantly associated with T2DM risk (HR=0.92, 95% CI 0.81-1.05), vigorous (15-25 MET-hours/week) and strenuous (25-50 MET-hours/week) levels of LTPA were associated with reduced incidence of T2DM (HR=0.83, 95% CI 0.74-0.94) and (HR=0.81,95% CI 0.72-0.91) respectively. LTPA beyond 50 MET-hours/week had no added benefit (HR=0.84, 95% CI 0.74-0.96). Conclusion: The study demonstrated the benefits of different LTPA levels in reducing the risk of T2DM in the MDC cohort. Moderate activity was not significantly associated while vigorous activity and above was significantly associated with reduced risk but there was no added benefit for LTPA beyond 50 MET-hours per week. The amount, This research was conducted to help understand how being active helps prevent diabetes and also how differently people from different social classes participate in leisure activities. The research showed that light activity (referred to moderate in the study) was not effective in preventing diabetes. However, vigorous and strenuous levels of activity were shown to help prevent diabetes but there was no added benefit of being active above strenuous levels. Exercise even in little amounts is beneficial to those who are overweight and obese. Social class affects people’s ability to participate in leisure activities and a low social class increases one risk to get diabetes. People who smoke or are former smokers also have a high risk of having diabetes compare to those who have never smokes. In conclusion, we found that being active is beneficial for health.

Published
2016

23. Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts

Author

Atabaki-Pasdar, Naeimeh, Ohlsson, Mattias, Viñuela, Ana, Frau, Francesca, Pomares-Millan, Hugo, Haid, Mark, Jones, Angus G., Thomas, E. Louise, Koivula, Robert W., Kurbasic, Azra, Mutie, Pascal M., Fitipaldi, Hugo, Fernandez, Juan, Dawed, Adem Y., Giordano, Giuseppe N., Forgie, Ian M., McDonald, Timothy J., Rutters, Femke, Cederberg, Henna, Chabanova, Elizaveta, Dale, Matilda, Masi, Federico De, Thomas, Cecilia Engel, Allin, Kristine H., Hansen, Tue H., Heggie, Alison, Hong, Mun-Gwan, Elders, Petra J. M., Kennedy, Gwen, Kokkola, Tarja, Pedersen, Helle Krogh, Mahajan, Anubha, McEvoy, Donna, Pattou, Francois, Raverdy, Violeta, Häussler, Ragna S., Sharma, Sapna, Thomsen, Henrik S., Vangipurapu, Jagadish, Vestergaard, Henrik, ‘T Hart, Leen M., Adamski, Jerzy, Musholt, Petra B., Brage, Soren, Brunak, Søren, Dermitzakis, Emmanouil, Frost, Gary, Hansen, Torben, Laakso, Markku, Pedersen, Oluf, Ridderstråle, Martin, Ruetten, Hartmut, Hattersley, Andrew T., Walker, Mark, Beulens, Joline W. J., Mari, Andrea, Schwenk, Jochen M., Gupta, Ramneek, McCarthy, Mark I., Pearson, Ewan R., Bell, Jimmy D., Pavo, Imre, and Franks, Paul W.

Subjects
Medicine and health sciences, Research and analysis methods, FOS: Computer and information sciences, Computer and information sciences, Biology and life sciences, 3. Good health, Research Article
Abstract

Funder: Henning och Johan Throne-Holsts, Funder: Hans Werthén, Funder: Swedish Foundation for Strategic Research, Funder: NIHR clinical senior lecturer fellowship, Funder: Wellcome Trust Senior Investigator, Funder: NIHR Exeter Clinical Research Facility, Funder: Science for Life Laboratory (Plasma Profiling Facility), Funder: Knut and Alice Wallenberg Foundation (Human Protein Atlas), Funder: Erling-Persson Foundation (KTH Centre for Precision Medicine), 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 (

24. Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts

Author

Atabaki-Pasdar, Naeimeh, Ohlsson, Mattias, Viñuela, Ana, Frau, Francesca, Pomares-Millan, Hugo, Haid, Mark, Jones, Angus G, Thomas, E Louise, Koivula, Robert W, Kurbasic, Azra, Mutie, Pascal M, Fitipaldi, Hugo, Fernandez, Juan, Dawed, Adem Y, Giordano, Giuseppe N, Forgie, Ian M, McDonald, Timothy J, Rutters, Femke, Cederberg, Henna, Chabanova, Elizaveta, Dale, Matilda, Masi, Federico De, Thomas, Cecilia Engel, Allin, Kristine H, Hansen, Tue H, Heggie, Alison, Hong, Mun-Gwan, Elders, Petra JM, Kennedy, Gwen, Kokkola, Tarja, Pedersen, Helle Krogh, Mahajan, Anubha, McEvoy, Donna, Pattou, Francois, Raverdy, Violeta, Häussler, Ragna S, Sharma, Sapna, Thomsen, Henrik S, Vangipurapu, Jagadish, Vestergaard, Henrik, 'T Hart, Leen M, Adamski, Jerzy, Musholt, Petra B, Brage, Soren, Brunak, Søren, Dermitzakis, Emmanouil, Frost, Gary, Hansen, Torben, Laakso, Markku, Pedersen, Oluf, Ridderstråle, Martin, Ruetten, Hartmut, Hattersley, Andrew T, Walker, Mark, Beulens, Joline WJ, Mari, Andrea, Schwenk, Jochen M, Gupta, Ramneek, McCarthy, Mark I, Pearson, Ewan R, Bell, Jimmy D, Pavo, Imre, and Franks, Paul W

Subjects
Diabetes Complications, Fatty Liver, Machine Learning, Male, Models, Statistical, Humans, Reproducibility of Results, Female, Prospective Studies, Middle Aged, Risk Assessment, 3. Good health
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 (

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