117 results on '"De Masi, Federico"'
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2. Discovery of drug–omics associations in type 2 diabetes with generative deep-learning models
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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
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- 2023
- Full Text
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3. Author Correction: Discovery of drug–omics associations in type 2 diabetes with generative deep-learning models
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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
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- 2023
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4. Four groups of type 2 diabetes contribute to the etiological and clinical heterogeneity in newly diagnosed individuals: An IMI DIRECT study
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Wesolowska-Andersen, Agata, Brorsson, Caroline A., Bizzotto, Roberto, Mari, Andrea, Tura, Andrea, Koivula, Robert, Mahajan, Anubha, Vinuela, Ana, Tajes, Juan Fernandez, Sharma, Sapna, Haid, Mark, Prehn, Cornelia, Artati, Anna, Hong, Mun-Gwan, Musholt, Petra B., Kurbasic, Azra, De Masi, Federico, Tsirigos, Kostas, Pedersen, Helle Krogh, Gudmundsdottir, Valborg, Thomas, Cecilia Engel, Banasik, Karina, Jennison, Chrisopher, Jones, Angus, Kennedy, Gwen, Bell, Jimmy, Thomas, Louise, Frost, Gary, Thomsen, Henrik, Allin, Kristine, Hansen, Tue Haldor, Vestergaard, Henrik, Hansen, Torben, Rutters, Femke, Elders, Petra, t’Hart, Leen, Bonnefond, Amelie, Canouil, Mickaël, Brage, Soren, Kokkola, Tarja, Heggie, Alison, McEvoy, Donna, Hattersley, Andrew, McDonald, Timothy, Teare, Harriet, Ridderstrale, Martin, Walker, Mark, Forgie, Ian, Giordano, Giuseppe N., Froguel, Philippe, Pavo, Imre, Ruetten, Hartmut, Pedersen, Oluf, Dermitzakis, Emmanouil, Franks, Paul W., Schwenk, Jochen M., Adamski, Jerzy, Pearson, Ewan, McCarthy, Mark I., and Brunak, Søren
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- 2022
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5. The Association of Cardiometabolic, Diet and Lifestyle Parameters With Plasma Glucagon-like Peptide-1: An IMI DIRECT Study
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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, De Masi, Federico, Tsirigos, Konstantinos D, Brunak, Søren, Viñuela, Ana, Mahajan, Anubha, McDonald, Timothy J, Kokkola, Tarja, Rutters, Femke, Beulens, Joline, Muilwijk, Mirthe, Blom, Marieke, Elders, Petra, Hansen, Tue H, Fernandez-Tajes, Juan, Jones, Angus, Jennison, Chris, Walker, Mark, McCarthy, Mark I, Pedersen, Oluf, Ruetten, Hartmut, Forgie, Ian, Holst, Jens J, Thomsen, Henrik S, Ridderstråle, Martin, Bell, Jimmy D, Adamski, Jerzy, Franks, Paul W, Hansen, Torben, Holmes, Elaine, Frost, Gary, and Pearson, Ewan R
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- 2024
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6. Genomic and drug target evaluation of 90 cardiovascular proteins in 30,931 individuals
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Folkersen, Lasse, Gustafsson, Stefan, Wang, Qin, Hansen, Daniel Hvidberg, Hedman, Åsa K., Schork, Andrew, Page, Karen, Zhernakova, Daria V., Wu, Yang, Peters, James, Eriksson, Niclas, Bergen, Sarah E., Boutin, Thibaud S., Bretherick, Andrew D., Enroth, Stefan, Kalnapenkis, Anette, Gådin, Jesper R., Suur, Bianca E., Chen, Yan, Matic, Ljubica, Gale, Jeremy D., Lee, Julie, Zhang, Weidong, Quazi, Amira, Ala-Korpela, Mika, Choi, Seung Hoan, Claringbould, Annique, Danesh, John, Davey Smith, George, de Masi, Federico, Elmståhl, Sölve, Engström, Gunnar, Fauman, Eric, Fernandez, Celine, Franke, Lude, Franks, Paul W., Giedraitis, Vilmantas, Haley, Chris, Hamsten, Anders, Ingason, Andres, Johansson, Åsa, Joshi, Peter K., Lind, Lars, Lindgren, Cecilia M., Lubitz, Steven, Palmer, Tom, Macdonald-Dunlop, Erin, Magnusson, Martin, Melander, Olle, Michaelsson, Karl, Morris, Andrew P., Mägi, Reedik, Nagle, Michael W., Nilsson, Peter M., Nilsson, Jan, Orho-Melander, Marju, Polasek, Ozren, Prins, Bram, Pålsson, Erik, Qi, Ting, Sjögren, Marketa, Sundström, Johan, Surendran, Praveen, Võsa, Urmo, Werge, Thomas, Wernersson, Rasmus, Westra, Harm-Jan, Yang, Jian, Zhernakova, Alexandra, Ärnlöv, Johan, Fu, Jingyuan, Smith, J. Gustav, Esko, Tõnu, Hayward, Caroline, Gyllensten, Ulf, Landen, Mikael, Siegbahn, Agneta, Wilson, James F., Wallentin, Lars, Butterworth, Adam S., Holmes, Michael V., Ingelsson, Erik, and Mälarstig, Anders
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- 2020
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7. The Interplay of Non-coding RNAs and X Chromosome Inactivation in Human Disease
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Russo, Francesco, De Masi, Federico, Brunak, Søren, Belling, Kirstine, Barciszewski, Jan, Series Editor, Rajewsky, Nikolaus, Series Editor, Erdmann, Volker A., Founding Editor, and Jurga, Stefan, editor
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- 2018
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8. Genetic studies of abdominal MRI data identify genes regulating hepcidin as major determinants of liver iron concentration
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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
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- 2019
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9. Ibuprofen alters human testicular physiology to produce a state of compensated hypogonadism
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Kristensen, David Møbjerg, Desdoits-Lethimonier, Christèle, Mackey, Abigail L., Dalgaard, Marlene Danner, De Masi, Federico, Munkbøl, Cecilie Hurup, Styrishave, Bjarne, Antignac, Jean-Philippe, Le Bizec, Bruno, Platel, Christian, Hay-Schmidt, Anders, Jensen, Tina Kold, Lesné, Laurianne, Mazaud-Guittot, Séverine, Kristiansen, Karsten, Brunak, Søren, Kjaer, Michael, Juul, Anders, and Jégou, Bernard
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- 2018
10. 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
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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
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- 2019
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11. Whole blood co-expression modules associate with metabolic traits and type 2 diabetes: an IMI-DIRECT study
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Gudmundsdottir, Valborg, Pedersen, Helle Krogh, Mazzoni, Gianluca, Allin, Kristine H., Artati, Anna, Beulens, Joline W., Banasik, Karina, Brorsson, Caroline, Cederberg, Henna, Chabanova, Elizaveta, De Masi, Federico, Elders, Petra J., Forgie, Ian, Giordano, Giuseppe N., Grallert, Harald, Gupta, Ramneek, Haid, Mark, Hansen, Torben, Hansen, Tue H., Hattersley, Andrew T., Heggie, Alison, Hong, Mun-Gwan, Jones, Angus G., Koivula, Robert, Kokkola, Tarja, Laakso, Markku, Løngreen, Peter, Mahajan, Anubha, Mari, Andrea, McDonald, Timothy J., McEvoy, Donna, Musholt, Petra B., Pavo, Imre, Prehn, Cornelia, Ruetten, Hartmut, Ridderstråle, Martin, Rutters, Femke, Sharma, Sapna, Slieker, Roderick C., Syed, Ali, Tajes, Juan Fernandez, Thomas, Cecilia Engel, Thomsen, Henrik S., Vangipurapu, Jagadish, Vestergaard, Henrik, Viñuela, Ana, Wesolowska-Andersen, Agata, Walker, Mark, Adamski, Jerzy, Schwenk, Jochen M., McCarthy, Mark I., Pearson, Ewan, Dermitzakis, Emmanouil, Franks, Paul W., Pedersen, Oluf, and Brunak, Søren
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- 2020
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12. HybriChip : an antigen microarray based screening tool, designed for a high-throughput production platform of mouse derived monoclonal antibodies
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De Masi, Federico
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571.9677 - Abstract
The incapacity to generate a sufficient number of mAbs to conduct large-scale analyses remains one of the most serious limitations in the field of functional genomics. Current methods of mAb production are very labour intensive and disappointingly low throughput, with a per capita production level of about 20 antigens per year. Here we present a modified hybridoma production method incorporating a novel screening assay which can be scaled up to generate antibodies in sufficient quantities for proteomics-scale analysis. Our method circumvents previous obstacles to increasing the throughput level of mAb production, in two ways. First, by immunizing a single mouse with multiple target antigens, we dramatically reduce the number of tissue culture operations normally necessary for performing multiple fusions simultaneously using only one antigen per animal. This minimises tissue culture operations and results in hybridomas that secrete antibodies specifically recognizing each of the target antigens. Second, we developed a novel antigen microarray assay (HybriChip) to screen supernatants generated by large-scale production. In this assay, an antigen chip is generated by coating an aminosilane treated slide with a single target antigen. Hybridoma culture supernatants from a fusion are consolidated and spotted as a microarray onto the antigen chip. After probing with a suitable fluorescently labelled secondary antibody, positive hybridomas are identified in a microarray scanner. The isotype of the bound antibody can be concomitantly determined by probing the antigen chip with mixtures of isotype-specific secondary antibodies, such as Cy5 -conjugated anti-mouse IgM and Cy3-conjugated antimouse pan-IgG (recognizing all mouse IgG isotypes). Different antigen chips can simultaneously be spotted in parallel with the same hybridoma culture supernatants, allowing rapid automated assay of multiple antibodies against many target antigens.
- Published
- 2004
13. Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models:[with Author Correction]
- Author
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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.
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- 2023
14. The governance structure for data access in the DIRECT consortium: an innovative medicines initiative (IMI) project
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Teare, Harriet J. A., de Masi, Federico, Banasik, Karina, Barnett, Anna, Herrgard, Sanna, Jablonka, Bernd, Postma, Jacqueline W. M., McDonald, Timothy J., Forgie, Ian, Chmura, Piotr J., Rydzka, Emil K., Gupta, Ramneek, Brunak, Soren, Pearson, Ewan, and Kaye, Jane
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- 2018
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15. Inferring causal pathways between metabolic processes and liver fat accumulation: an IMI DIRECT study
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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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16. Correction to: The role of physical activity in metabolic homeostasis before and after the onset of type 2 diabetes: an IMI DIRECT study (Diabetologia, (2020), 63, 4, (744-756), 10.1007/s00125-019-05083-6)
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Koivula, Robert W., Atabaki-Pasdar, Naeimeh, Giordano, Giuseppe N., White, Tom, Adamski, Jerzy, Bell, Jimmy D., Beulens, Joline, Brage, S. ren, Brunak, S. ren, de Masi, Federico, Dermitzakis, Emmanouil T., Forgie, Ian M., Frost, Gary, Hansen, Torben, Hansen, Tue H., Hattersley, Andrew, Kokkola, Tarja, Kurbasic, Azra, Laakso, Markku, Mari, Andrea, McDonald, Timothy J., Pedersen, Oluf, Rutters, Femke, Schwenk, Jochen M., Teare, Harriet J. A., Thomas, E. Louise, Vinuela, Ana, Mahajan, Anubha, McCarthy, Mark I., Ruetten, Hartmut, Walker, Mark, Pearson, Ewan, Pavo, Imre, Franks, Paul W., Epidemiology and Data Science, ACS - Diabetes & metabolism, APH - Health Behaviors & Chronic Diseases, APH - Aging & Later Life, and ACS - Heart failure & arrhythmias
- Abstract
Unfortunately, ‘Present address’ was omitted from one of the addresses provided for Mark I. McCarthy (#26). The corrected address details are given on the following page.
- Published
- 2021
17. A DNA-binding-site landscape and regulatory network analysis for NAC transcription factors in Arabidopsis thaliana
- Author
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Lindemose, Søren, Jensen, Michael K., Van de Velde, Jan, OʼShea, Charlotte, Heyndrickx, Ken S., Workman, Christopher T., Vandepoele, Klaas, Skriver, Karen, and De Masi, Federico
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- 2014
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18. The role of physical activity in metabolic homeostasis before and after the onset of type 2 diabetes: an IMI DIRECT study
- Author
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Koivula, Robert W, Atabaki-Pasdar, Naeimeh, Giordano, Giuseppe N, White, Tom, Adamski, Jerzy, Bell, Jimmy D, Beulens, Joline, Brage, Søren, Brunak, Søren, De Masi, Federico, Dermitzakis, Emmanouil T, Forgie, Ian M, Frost, Gary, Hansen, Torben, Hansen, Tue H, Hattersley, Andrew, Kokkola, Tarja, Kurbasic, Azra, Laakso, Markku, Mari, Andrea, McDonald, Timothy J, Pedersen, Oluf, Rutters, Femke, Schwenk, Jochen M, Teare, Harriet JA, Thomas, E Louise, Vinuela, Ana, Mahajan, Anubha, McCarthy, Mark I, Ruetten, Hartmut, Walker, Mark, Pearson, Ewan, Pavo, Imre, Franks, Paul W, IMI DIRECT Consortium, Koivula, Robert W [0000-0002-1646-4163], and Apollo - University of Cambridge Repository
- Subjects
Blood Glucose ,Male ,Denmark ,Glycemic Control ,Ectopic fat ,Cohort Studies ,Glycaemic control ,Homeostasis ,Humans ,Exercise ,Finland ,Aged ,Netherlands ,Sweden ,Physical activity ,Beta cell function ,Type 2 diabetes ,Glucose Tolerance Test ,Middle Aged ,Insulin sensitivity ,Cross-Sectional Studies ,Diabetes Mellitus, Type 2 ,Structural equation modelling ,Female ,Insulin Resistance ,Energy Metabolism ,Prediabetes - Abstract
AIMS/HYPOTHESIS: It is well established that physical activity, abdominal ectopic fat and glycaemic regulation are related but the underlying structure of these relationships is unclear. The previously proposed twin-cycle hypothesis (TC) provides a mechanistic basis for impairment in glycaemic control through the interactions of substrate availability, substrate metabolism and abdominal ectopic fat accumulation. Here, we hypothesise that the effect of physical activity in glucose regulation is mediated by the twin-cycle. We aimed to examine this notion in the Innovative Medicines Initiative Diabetes Research on Patient Stratification (IMI DIRECT) Consortium cohorts comprised of participants with normal or impaired glucose regulation (cohort 1: N ≤ 920) or with recently diagnosed type 2 diabetes (cohort 2: N ≤ 435). METHODS: We defined a structural equation model that describes the TC and fitted this within the IMI DIRECT dataset. A second model, twin-cycle plus physical activity (TC-PA), to assess the extent to which the effects of physical activity in glycaemic regulation are mediated by components in the twin-cycle, was also fitted. Beta cell function, insulin sensitivity and glycaemic control were modelled from frequently sampled 75 g OGTTs (fsOGTTs) and mixed-meal tolerance tests (MMTTs) in participants without and with diabetes, respectively. Abdominal fat distribution was assessed using MRI, and physical activity through wrist-worn triaxial accelerometry. Results are presented as standardised beta coefficients, SE and p values, respectively. RESULTS: The TC and TC-PA models showed better fit than null models (TC: χ2 = 242, p = 0.004 and χ2 = 63, p = 0.001 in cohort 1 and 2, respectively; TC-PA: χ2 = 180, p = 0.041 and χ2 = 60, p = 0.008 in cohort 1 and 2, respectively). The association of physical activity with glycaemic control was primarily mediated by variables in the liver fat cycle. CONCLUSIONS/INTERPRETATION: These analyses partially support the mechanisms proposed in the twin-cycle model and highlight mechanistic pathways through which insulin sensitivity and liver fat mediate the association between physical activity and glycaemic control.
- Published
- 2020
19. Using a structural and logics systems approach to infer bHLH–DNA binding specificity determinants
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De Masi, Federico, Grove, Christian A., Vedenko, Anastasia, Alibés, Andreu, Gisselbrecht, Stephen S., Serrano, Luis, Bulyk, Martha L., and Walhout, Albertha J. M.
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- 2011
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20. Genome-Wide Association Analysis of Pancreatic Beta-Cell Glucose Sensitivity
- Author
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Deshmukh, Harshal A, primary, Madsen, Anne Lundager, additional, Viñuela, Ana, additional, Have, Christian Theil, additional, Grarup, Niels, additional, Tura, Andrea, additional, Mahajan, Anubha, additional, Heggie, Alison J, additional, Koivula, Robert W, additional, De Masi, Federico, additional, Tsirigos, Konstantinos K, additional, Linneberg, Allan, additional, Drivsholm, Thomas, additional, Pedersen, Oluf, additional, Sørensen, Thorkild I A, additional, Astrup, Arne, additional, Gjesing, Anette A P, additional, Pavo, Imre, additional, Wood, Andrew R, additional, Ruetten, Hartmut, additional, Jones, Angus G, additional, Koopman, Anitra D M, additional, Cederberg, Henna, additional, Rutters, Femke, additional, Ridderstrale, Martin, additional, Laakso, Markku, additional, McCarthy, Mark I, additional, Frayling, Tim M, additional, Ferrannini, Ele, additional, Franks, Paul W, additional, Pearson, Ewan R, additional, Mari, Andrea, additional, Hansen, Torben, additional, and Walker, Mark, additional
- Published
- 2020
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- View/download PDF
21. Dietary metabolite profiling brings new insight into the relationship between nutrition and metabolic risk: An IMI DIRECT study
- Author
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Eriksen, Rebeca, primary, Perez, Isabel Garcia, additional, Posma, Joram M., additional, Haid, Mark, additional, Sharma, Sapna, additional, Prehn, Cornelia, additional, Thomas, Louise E., additional, Koivula, Robert W., additional, Bizzotto, Roberto, additional, Mari, Andrea, additional, Giordano, Giuseppe N., additional, Pavo, Imre, additional, Schwenk, Jochen M., additional, De Masi, Federico, additional, Tsirigos, Konstantinos D., additional, Brunak, Søren, additional, Viñuela, Ana, additional, Mahajan, Anubha, additional, McDonald, Timothy J., additional, Kokkola, Tarja, additional, Rutter, Femke, additional, Teare, Harriet, additional, Hansen, Tue H., additional, Fernandez, Juan, additional, Jones, Angus, additional, Jennison, Chris, additional, Walker, Mark, additional, McCarthy, Mark I., additional, Pedersen, Oluf, additional, Ruetten, Hartmut, additional, Forgie, Ian, additional, Bell, Jimmy D., additional, Pearson, Ewan R., additional, Franks, Paul W., additional, Adamski, Jerzy, additional, Holmes, Elaine, additional, and Frost, Gary, additional
- Published
- 2020
- Full Text
- View/download PDF
22. Genomic evaluation of circulating proteins for drug target characterisation and precision medicine
- Author
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Folkersen, Lasse, primary, Gustafsson, Stefan, additional, Wang, Qin, additional, Hansen, Daniel Hvidberg, additional, Hedman, Åsa K, additional, Schork, Andrew, additional, Page, Karen, additional, Zhernakova, Daria V, additional, Wu, Yang, additional, Peters, James, additional, Ericsson, Niclas, additional, Bergen, Sarah E, additional, Boutin, Thibaud, additional, Bretherick, Andrew D, additional, Enroth, Stefan, additional, Kalnapenkis, Anettne, additional, Gådin, Jesper R, additional, Suur, Bianca, additional, Chen, Yan, additional, Matic, Ljubica, additional, Gale, Jeremy D, additional, Lee, Julie, additional, Zhang, Weidong, additional, Quazi, Amira, additional, Ala-Korpela, Mika, additional, Choi, Seung Hoan, additional, Claringbould, Annique, additional, Danesh, John, additional, Davey-Smith, George, additional, de Masi, Federico, additional, Elmståhl, Sölve, additional, Engström, Gunnar, additional, Fauman, Eric, additional, Fernandez, Celine, additional, Franke, Lude, additional, Franks, Paul, additional, Giedraitis, Vilmantas, additional, Haley, Chris, additional, Hamsten, Anders, additional, Ingason, Andres, additional, Johansson, Åsa, additional, Joshi, Peter K, additional, Lind, Lars, additional, Lindgren, Cecilia M., additional, Lubitz, Steven, additional, Palmer, Tom, additional, Macdonald-Dunlop, Erin, additional, Magnusson, Martin, additional, Melander, Olle, additional, Michaelsson, Karl, additional, Morris, Andrew P., additional, Mägi, Reedik, additional, Nagle, Michael, additional, Nilsson, Peter M, additional, Nilsson, Jan, additional, Orho-Melander, Marju, additional, Polasek, Ozren, additional, Prins, Bram, additional, Pålsson, Erik, additional, Qi, Ting, additional, Sjögren, Marketa, additional, Sundström, Johan, additional, Surendran, Praveen, additional, Võsa, Urmo, additional, Werge, Thomas, additional, Wernersson, Rasmus, additional, Westra, Harm-Jan, additional, Yang, Jian, additional, Zhernakova, Alexandra, additional, Ärnlöv, Johan, additional, Fu, Jingyuan, additional, Smith, Gustav, additional, Esko, Tonu, additional, Hayward, Caroline, additional, Gyllensten, Ulf, additional, Landen, Mikael, additional, Siegbahn, Agneta, additional, Wilson, Jim F, additional, Wallentin, Lars, additional, Butterworth, Adam S, additional, Holmes, Michael V, additional, Ingelsson, Erik, additional, and Mälarstig, Anders, additional
- Published
- 2020
- Full Text
- View/download PDF
23. Predicting and elucidating the etiology of fatty liver disease using a machine learning-based approach: an IMI DIRECT study
- Author
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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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- View/download PDF
24. Using protein design algorithms to understand the molecular basis of disease caused by protein–DNA interactions: the Pax6 example
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Alibés, Andreu, Nadra, Alejandro D., De Masi, Federico, Bulyk, Martha L., Serrano, Luis, and Stricher, François
- Published
- 2010
- Full Text
- View/download PDF
25. Genetic studies of abdominal MRI data identify genes regulating hepcidin as major determinants of liver iron concentration
- Author
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Wilman, Henry R., Parisinos, Constantinos A., Atabaki-Pasdar, Naeimeh, Kelly, Matt, Thomas, E. Louise, Neubauer, Stefan, 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, 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, Koopman, Anitra, Rutters, Femke, Beulens, Joline, Groeneveld, Lenka, Thomas, Louise, Whitcher, Brandon, Mahajan, Anubha, Hingorani, Aroon D., Patel, Riyaz S., Hemingway, Harry, Franks, Paul W., Bell, Jimmy D., Banerjee, Rajarshi, Yaghootkar, Hanieh, Epidemiology and Data Science, APH - Health Behaviors & Chronic Diseases, ACS - Diabetes & metabolism, ACS - Heart failure & arrhythmias, and APH - Aging & Later Life
- Abstract
Background & Aims: Excess liver iron content is common and is linked to the risk of hepatic and extrahepatic diseases. We aimed to identify genetic variants influencing liver iron content and use genetics to understand its link to other traits and diseases. Methods: First, we performed a genome-wide association study (GWAS) in 8,289 individuals from UK Biobank, whose liver iron level had been quantified by magnetic resonance imaging, before validating our findings in an independent cohort (n = 1,513 from IMI DIRECT). Second, we used Mendelian randomisation to test the causal effects of 25 predominantly metabolic traits on liver iron content. Third, we tested phenome-wide associations between liver iron variants and 770 traits and disease outcomes. Results: We identified 3 independent genetic variants (rs1800562 [C282Y] and rs1799945 [H63D] in HFE and rs855791 [V736A] in TMPRSS6) associated with liver iron content that reached the GWAS significance threshold (p
- Published
- 2019
26. Profiles of Glucose Metabolism in Different Prediabetes Phenotypes, Classified by Fasting Glycemia, 2-Hour OGTT, Glycated Hemoglobin, and 1-Hour OGTT: An IMI DIRECT Study.
- Author
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Tura, Andrea, Grespan, Eleonora, Göbl, Christian S., Koivula, Robert W., Franks, Paul W., Pearson, Ewan R., Walker, Mark, Forgie, Ian M., Giordano, Giuseppe N., Pavo, Imre, Ruetten, Hartmut, Dermitzakis, Emmanouil T., McCarthy, Mark I., Pedersen, Oluf, Schwenk, Jochen M., Adamski, Jerzy, De Masi, Federico, Tsirigos, Konstantinos D., Brunak, Søren, and Viñuela, Ana
- Subjects
GLYCOSYLATED hemoglobin ,GLUCOSE metabolism ,INSULIN sensitivity ,PREDIABETIC state ,TYPE 2 diabetes - Abstract
Differences in glucose metabolism among categories of prediabetes have not been systematically investigated. In this longitudinal study, participants (N = 2,111) underwent a 2-h 75-g oral glucose tolerance test (OGTT) at baseline and 48 months. HbA1c was also measured. We classified participants as having isolated prediabetes defect (impaired fasting glucose [IFG], impaired glucose tolerance [IGT], or HbA1c indicative of prediabetes [IA1c]), two defects (IFG+IGT, IFG+IA1c, or IGT+IA1c), or all defects (IFG+IGT+IA1c). β-Cell function (BCF) and insulin sensitivity were assessed from OGTT. At baseline, in pooling of participants with isolated defects, they showed impairment in both BCF and insulin sensitivity compared with healthy control subjects. Pooled groups with two or three defects showed progressive further deterioration. Among groups with isolated defect, those with IGT showed lower insulin sensitivity, insulin secretion at reference glucose (ISRr), and insulin secretion potentiation (P < 0.002). Conversely, those with IA1c showed higher insulin sensitivity and ISRr (P < 0.0001). Among groups with two defects, we similarly found differences in both BCF and insulin sensitivity. At 48 months, we found higher type 2 diabetes incidence for progressively increasing number of prediabetes defects (odds ratio >2, P < 0.008). In conclusion, the prediabetes groups showed differences in type/degree of glucometabolic impairment. Compared with the pooled group with isolated defects, those with double or triple defect showed progressive differences in diabetes incidence. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
27. Ibuprofen alters human testicular physiology to produce a state of compensated hypogonadism
- Author
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Mackey, Abigail L., Dalgaard, Marlene Danner, De Masi, Federico, Munkbol, Cecilie Hurup, Styrishave, Bjarne, Antignac, Jean-Philippe, Le Bizec, Bruno, Platel, Christian, Hay-Schmidt, Anders, Jensen, Tina Kold, Lesne, Laurianne, Mazaud-Guittot, Séverine, Kristiansen, Karsten, Brunak, Soren, Kjaer, Michael, Juul, Anders, Jégou, Bernard, Kristensen, David Mobjerg, and Desdoits-Lethimonier, Christèle
- Subjects
ibuprofen ,endocrine disruption ,reproduction ,hypogonadism ,endocrinology - Abstract
Concern has been raised over increased male reproductive disorders in the Western world, and the disruption of male endocrinology has been suggested to play a central role. Several studies have shown that mild analgesics exposure during fetal life is associated with antiandrogenic effects and congenital malformations, but the effects on the adult man remain largely unknown. Through a clinical trial with young men exposed to ibuprofen, we show that the analgesic resulted in the clinical condition named "compensated hypogonadism," a condition prevalent among elderly men and associated with reproductive and physical disorders. In the men, luteinizing hormone (LH) and ibuprofen plasma levels were positively correlated, and the testosterone/LH ratio decreased. Using adult testis explants exposed or not exposed to ibuprofen, we demonstrate that the endocrine capabilities from testicular Leydig and Sertoli cells, including testosterone production, were suppressed through transcriptional repression. This effect was also observed in a human steroidogenic cell line. Our data demonstrate that ibuprofen alters the endocrine system via selective transcriptional repression in the human testes, thereby inducing compensated hypogonadism.
- Published
- 2018
28. Genome-Wide Association Analysis of Pancreatic Beta-Cell Glucose Sensitivity.
- Author
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Deshmukh, Harshal A, Madsen, Anne Lundager, Viñuela, Ana, Have, Christian Theil, Grarup, Niels, Tura, Andrea, Mahajan, Anubha, Heggie, Alison J, Koivula, Robert W, De Masi, Federico, Tsirigos, Konstantinos K, Linneberg, Allan, Drivsholm, Thomas, Pedersen, Oluf, Sørensen, Thorkild I A, Astrup, Arne, Gjesing, Anette A P, Pavo, Imre, Wood, Andrew R, and Ruetten, Hartmut
- Subjects
PANCREATIC beta cells ,GLUCOSE tolerance tests ,TYPE 2 diabetes ,GLUCOSE intolerance ,RESEARCH ,SEQUENCE analysis ,META-analysis ,RESEARCH methodology ,GENETIC polymorphisms ,CASE-control method ,EVALUATION research ,COMPARATIVE studies ,DIAGNOSIS ,DIGESTIVE organs ,DISEASE susceptibility ,RESEARCH funding ,GLUCOSE ,PREDIABETIC state ,LONGITUDINAL method - Abstract
Context: Pancreatic beta-cell glucose sensitivity is the slope of the plasma glucose-insulin secretion relationship and is a key predictor of deteriorating glucose tolerance and development of type 2 diabetes. However, there are no large-scale studies looking at the genetic determinants of beta-cell glucose sensitivity.Objective: To understand the genetic determinants of pancreatic beta-cell glucose sensitivity using genome-wide meta-analysis and candidate gene studies.Design: We performed a genome-wide meta-analysis for beta-cell glucose sensitivity in subjects with type 2 diabetes and nondiabetic subjects from 6 independent cohorts (n = 5706). Beta-cell glucose sensitivity was calculated from mixed meal and oral glucose tolerance tests, and its associations between known glycemia-related single nucleotide polymorphisms (SNPs) and genome-wide association study (GWAS) SNPs were estimated using linear regression models.Results: Beta-cell glucose sensitivity was moderately heritable (h2 ranged from 34% to 55%) using SNP and family-based analyses. GWAS meta-analysis identified multiple correlated SNPs in the CDKAL1 gene and GIPR-QPCTL gene loci that reached genome-wide significance, with SNP rs2238691 in GIPR-QPCTL (P value = 2.64 × 10-9) and rs9368219 in the CDKAL1 (P value = 3.15 × 10-9) showing the strongest association with beta-cell glucose sensitivity. These loci surpassed genome-wide significance when the GWAS meta-analysis was repeated after exclusion of the diabetic subjects. After correction for multiple testing, glycemia-associated SNPs in or near the HHEX and IGF2B2 loci were also associated with beta-cell glucose sensitivity.Conclusion: We show that, variation at the GIPR-QPCTL and CDKAL1 loci are key determinants of pancreatic beta-cell glucose sensitivity. [ABSTRACT FROM AUTHOR]- Published
- 2021
- Full Text
- View/download PDF
29. The role of physical activity in metabolic homeostasis before and after the onset of type 2 diabetes: an IMI DIRECT study.
- Author
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Koivula, Robert W., Atabaki-Pasdar, Naeimeh, Giordano, Giuseppe N., White, Tom, Adamski, Jerzy, Bell, Jimmy D., Beulens, Joline, Brage, Søren, Brunak, Søren, De Masi, Federico, Dermitzakis, Emmanouil T., Forgie, Ian M., Frost, Gary, Hansen, Torben, Hansen, Tue H., Hattersley, Andrew, Kokkola, Tarja, Kurbasic, Azra, Laakso, Markku, and Mari, Andrea
- Abstract
Aims/hypothesis: It is well established that physical activity, abdominal ectopic fat and glycaemic regulation are related but the underlying structure of these relationships is unclear. The previously proposed twin-cycle hypothesis (TC) provides a mechanistic basis for impairment in glycaemic control through the interactions of substrate availability, substrate metabolism and abdominal ectopic fat accumulation. Here, we hypothesise that the effect of physical activity in glucose regulation is mediated by the twin-cycle. We aimed to examine this notion in the Innovative Medicines Initiative Diabetes Research on Patient Stratification (IMI DIRECT) Consortium cohorts comprised of participants with normal or impaired glucose regulation (cohort 1: N ≤ 920) or with recently diagnosed type 2 diabetes (cohort 2: N ≤ 435). Methods: We defined a structural equation model that describes the TC and fitted this within the IMI DIRECT dataset. A second model, twin-cycle plus physical activity (TC-PA), to assess the extent to which the effects of physical activity in glycaemic regulation are mediated by components in the twin-cycle, was also fitted. Beta cell function, insulin sensitivity and glycaemic control were modelled from frequently sampled 75 g OGTTs (fsOGTTs) and mixed-meal tolerance tests (MMTTs) in participants without and with diabetes, respectively. Abdominal fat distribution was assessed using MRI, and physical activity through wrist-worn triaxial accelerometry. Results are presented as standardised beta coefficients, SE and p values, respectively. Results: The TC and TC-PA models showed better fit than null models (TC: χ
2 = 242, p = 0.004 and χ2 = 63, p = 0.001 in cohort 1 and 2, respectively; TC-PA: χ2 = 180, p = 0.041 and χ2 = 60, p = 0.008 in cohort 1 and 2, respectively). The association of physical activity with glycaemic control was primarily mediated by variables in the liver fat cycle. Conclusions/interpretation: These analyses partially support the mechanisms proposed in the twin-cycle model and highlight mechanistic pathways through which insulin sensitivity and liver fat mediate the association between physical activity and glycaemic control. [ABSTRACT FROM AUTHOR]- Published
- 2020
- Full Text
- View/download PDF
30. High-throughput epitope profiling of snake venom toxins:unveiling the complexity of antigen-antibody interactions of antivenoms
- Author
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Engmark, Mikael, Andersen, Mikael Rørdam, Laustsen, Andreas Hougaard, Patel, Jigar, Sullivan, Eric, De Masi, Federico, Hansen, Christian Skjødt, Kringelum, Jens Vindahl, Lomonte, Bruno, Gutiérrez, José María, and Lund, Ole
- Subjects
complex mixtures - Abstract
Insight into the molecular details of polyclonal antivenom antibody specificity is a prerequisite for accurate prediction of cross-reactivity and can provide a basis for design of novel antivenoms. In this work, a highthroughput approach was applied to characterize linear elements in epitopes in 82 toxins from four African mamba and three neurotoxic cobra snakes obtained from public databases.
- Published
- 2016
31. miRandola 2017: a curated knowledge base of non-invasive biomarkers
- Author
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Russo, Francesco, primary, Di Bella, Sebastiano, additional, Vannini, Federica, additional, Berti, Gabriele, additional, Scoyni, Flavia, additional, Cook, Helen V, additional, Santos, Alberto, additional, Nigita, Giovanni, additional, Bonnici, Vincenzo, additional, Laganà, Alessandro, additional, Geraci, Filippo, additional, Pulvirenti, Alfredo, additional, Giugno, Rosalba, additional, De Masi, Federico, additional, Belling, Kirstine, additional, Jensen, Lars J, additional, Brunak, Søren, additional, Pellegrini, Marco, additional, and Ferro, Alfredo, additional
- Published
- 2017
- Full Text
- View/download PDF
32. Cross-recognition of a pit viper (Crotalinae) polyspecific antivenom explored through high-density peptide microarray epitope mapping
- Author
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Engmark, Mikael, primary, Lomonte, Bruno, additional, Gutiérrez, José María, additional, Laustsen, Andreas H., additional, De Masi, Federico, additional, Andersen, Mikael R., additional, and Lund, Ole, additional
- Published
- 2017
- Full Text
- View/download PDF
33. High-throughput immuno-profiling of mamba (Dendroaspis) venom toxin epitopes using high-density peptide microarrays
- Author
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Engmark, Mikael, primary, Andersen, Mikael R., additional, Laustsen, Andreas H., additional, Patel, Jigar, additional, Sullivan, Eric, additional, de Masi, Federico, additional, Hansen, Christian S., additional, Kringelum, Jens V., additional, Lomonte, Bruno, additional, Gutiérrez, José María, additional, and Lund, Ole, additional
- Published
- 2016
- Full Text
- View/download PDF
34. Control of lysosomal biogenesis and Notch-dependent tissue patterning by components of the TFEB-V-ATPase axis inDrosophila melanogaster
- Author
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Tognon, Emiliana, primary, Kobia, Francis, additional, Busi, Ilaria, additional, Fumagalli, Arianna, additional, De Masi, Federico, additional, and Vaccari, Thomas, additional
- Published
- 2016
- Full Text
- View/download PDF
35. ATAF1 transcription factor directly regulates abscisic acid biosynthetic geneNCED3inArabidopsis thaliana
- Author
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Jensen, Michael Krogh, primary, Lindemose, Søren, additional, de Masi, Federico, additional, Reimer, Julia J., additional, Nielsen, Michael, additional, Perera, Venura, additional, Workman, Chris T., additional, Turck, Franziska, additional, Grant, Murray R., additional, Mundy, John, additional, Petersen, Morten, additional, and Skriver, Karen, additional
- Published
- 2013
- Full Text
- View/download PDF
36. Control of lysosomal biogenesis and Notch-dependent tissue patterning by components of the TFEB-V-ATPase axis in Drosophila melanogaster.
- Author
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Tognon, Emiliana, Kobia, Francis, Busi, Ilaria, Fumagalli, Arianna, De Masi, Federico, and Vaccari, Thomas
- Published
- 2016
- Full Text
- View/download PDF
37. A Multiparameter Network Reveals Extensive Divergence between C. elegans bHLH Transcription Factors
- Author
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Grove, Christian A., primary, De Masi, Federico, additional, Barrasa, M. Inmaculada, additional, Newburger, Daniel E., additional, Alkema, Mark J., additional, Bulyk, Martha L., additional, and Walhout, Albertha J.M., additional
- Published
- 2009
- Full Text
- View/download PDF
38. High-resolution DNA-binding specificity analysis of yeast transcription factors
- Author
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Zhu, Cong, primary, Byers, Kelsey J.R.P., additional, McCord, Rachel Patton, additional, Shi, Zhenwei, additional, Berger, Michael F., additional, Newburger, Daniel E., additional, Saulrieta, Katrina, additional, Smith, Zachary, additional, Shah, Mita V., additional, Radhakrishnan, Mathangi, additional, Philippakis, Anthony A., additional, Hu, Yanhui, additional, De Masi, Federico, additional, Pacek, Marcin, additional, Rolfs, Andreas, additional, Murthy, Tal, additional, LaBaer, Joshua, additional, and Bulyk, Martha L., additional
- Published
- 2009
- Full Text
- View/download PDF
39. High throughput production of mouse monoclonal antibodies using antigen microarrays
- Author
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De Masi, Federico, primary, Chiarella, Pieranna, additional, Wilhelm, Heike, additional, Massimi, Marzia, additional, Bullard, Belinda, additional, Ansorge, Wilhelm, additional, and Sawyer, Alan, additional
- Published
- 2005
- Full Text
- View/download PDF
40. Active microwave soil moisture sensing under vegetation cover
- Author
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Troch, Peter A., primary, Su, Zhongbo, additional, Colombo, P., additional, and De Masi, Federico, additional
- Published
- 1997
- Full Text
- View/download PDF
41. ATAF1 transcription factor directly regulates abscisic acid biosynthetic gene NCED3 in Arabidopsis thaliana.
- Author
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Jensen, Michael Krogh, Lindemose, Søren, de Masi, Federico, Reimer, Julia J., Nielsen, Michael, Perera, Venura, Workman, Chris T., Turck, Franziska, Grant, Murray R., Mundy, John, Petersen, Morten, and Skriver, Karen
- Subjects
TRANSCRIPTION factors ,ABSCISIC acid ,ARABIDOPSIS thaliana ,PLANT adaptation ,PLANT development - Abstract
ATAF1, an Arabidopsis thaliana NAC transcription factor, plays important roles in plant adaptation to environmental stress and development. To search for ATAF1 target genes, we used protein binding microarrays and chromatin‐immunoprecipitation (ChIP). This identified T[A,C,G]CGT[A,G] and TT[A,C,G]CGT as ATAF1 consensus binding sequences. Co‐expression analysis across publicly available microarray experiments identified 25 genes co‐expressed with ATAF1. The promoter regions of ATAF1 co‐expressors were significantly enriched for ATAF1 binding sites, and TTGCGTA was identified in the promoter of the key abscisic acid (ABA) phytohormone biosynthetic gene NCED3. ChIP‐qPCR and expression analysis showed that ATAF1 binding to the NCED3 promoter correlated with increased NCED3 expression and ABA hormone levels. These results indicate that ATAF1 regulates ABA biosynthesis. [ABSTRACT FROM AUTHOR]
- Published
- 2013
- Full Text
- View/download PDF
42. 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
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Koivula, Robert W, Forgie, Ian M, Kurbasic, Azra, Viñuela, Ana, Heggie, Alison, Giordano, Giuseppe N, Hansen, Tue H, Hudson, Michelle, Koopman, Anitra DM, 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 JA, 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 IMI DIRECT Consortium
- Subjects
Blood Glucose ,Male ,Genome ,Physical activity ,Insulin secretion ,Type 2 diabetes ,Glucose Tolerance Test ,Middle Aged ,Insulin sensitivity ,Ectopic fat ,Metformin ,3. Good health ,Diet ,Cohort Studies ,Prediabetic State ,Glucose ,Diabetes Mellitus, Type 2 ,Glycaemic control ,Humans ,Personalised medicine ,Female ,Prospective Studies ,Prediabetes ,Biomarkers ,Aged - Abstract
AIMS/HYPOTHESIS: Here, we describe the characteristics of the Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) epidemiological cohorts at baseline and follow-up examinations (18, 36 and 48 months of follow-up). METHODS: From a sampling frame of 24,682 adults of European ancestry enrolled in population-based cohorts across Europe, participants at varying risk of glycaemic deterioration were identified using a risk prediction algorithm (based on age, BMI, waist circumference, use of antihypertensive medication, smoking status and parental history of type 2 diabetes) and enrolled into a prospective cohort study (n = 2127) (cohort 1, prediabetes risk). We also recruited people from clinical registries with type 2 diabetes diagnosed 6-24 months previously (n = 789) into a second cohort study (cohort 2, diabetes). Follow-up examinations took place at ~18 months (both cohorts) and at ~48 months (cohort 1) or ~36 months (cohort 2) after baseline examinations. The cohorts were studied in parallel using matched protocols across seven clinical centres in northern Europe. RESULTS: Using ADA 2011 glycaemic categories, 33% (n = 693) of cohort 1 (prediabetes risk) had normal glucose regulation and 67% (n = 1419) had impaired glucose regulation. Seventy-six per cent of participants in cohort 1 was male. Cohort 1 participants had the following characteristics (mean ± SD) at baseline: age 62 (6.2) years; BMI 27.9 (4.0) kg/m2; fasting glucose 5.7 (0.6) mmol/l; 2 h glucose 5.9 (1.6) mmol/l. At the final follow-up examination the participants' clinical characteristics were as follows: fasting glucose 6.0 (0.6) mmol/l; 2 h OGTT glucose 6.5 (2.0) mmol/l. In cohort 2 (diabetes), 66% (n = 517) were treated by lifestyle modification and 34% (n = 272) were treated with metformin plus lifestyle modification at enrolment. Fifty-eight per cent of participants in cohort 2 was male. Cohort 2 participants had the following characteristics at baseline: age 62 (8.1) years; BMI 30.5 (5.0) kg/m2; fasting glucose 7.2 (1.4) mmol/l; 2 h glucose 8.6 (2.8) mmol/l. At the final follow-up examination, the participants' clinical characteristics were as follows: fasting glucose 7.9 (2.0) mmol/l; 2 h mixed-meal tolerance test glucose 9.9 (3.4) mmol/l. CONCLUSIONS/INTERPRETATION: The IMI DIRECT cohorts are intensely characterised, with a wide-variety of metabolically relevant measures assessed prospectively. We anticipate that the cohorts, made available through managed access, will provide a powerful resource for biomarker discovery, multivariate aetiological analyses and reclassification of patients for the prevention and treatment of type 2 diabetes.
43. The role of physical activity in metabolic homeostasis before and after the onset of type 2 diabetes: an IMI DIRECT study
- Author
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Koivula, Robert W, Atabaki-Pasdar, Naeimeh, Giordano, Giuseppe N, White, Tom, Adamski, Jerzy, Bell, Jimmy D, Beulens, Joline, Brage, Søren, Brunak, Søren, De Masi, Federico, Dermitzakis, Emmanouil T, Forgie, Ian M, Frost, Gary, Hansen, Torben, Hansen, Tue H, Hattersley, Andrew, Kokkola, Tarja, Kurbasic, Azra, Laakso, Markku, Mari, Andrea, McDonald, Timothy J, Pedersen, Oluf, Rutters, Femke, Schwenk, Jochen M, Teare, Harriet JA, Thomas, E Louise, Vinuela, Ana, Mahajan, Anubha, McCarthy, Mark I, Ruetten, Hartmut, Walker, Mark, Pearson, Ewan, Pavo, Imre, Franks, Paul W, and IMI DIRECT Consortium
- Subjects
Blood Glucose ,Male ,Denmark ,Glycemic Control ,Ectopic fat ,Cohort Studies ,Glycaemic control ,Homeostasis ,Humans ,Exercise ,Finland ,Aged ,Netherlands ,Sweden ,Physical activity ,Beta cell function ,Type 2 diabetes ,Glucose Tolerance Test ,Middle Aged ,Insulin sensitivity ,3. Good health ,Cross-Sectional Studies ,Diabetes Mellitus, Type 2 ,Structural equation modelling ,Female ,Insulin Resistance ,Energy Metabolism ,Prediabetes - Abstract
AIMS/HYPOTHESIS: It is well established that physical activity, abdominal ectopic fat and glycaemic regulation are related but the underlying structure of these relationships is unclear. The previously proposed twin-cycle hypothesis (TC) provides a mechanistic basis for impairment in glycaemic control through the interactions of substrate availability, substrate metabolism and abdominal ectopic fat accumulation. Here, we hypothesise that the effect of physical activity in glucose regulation is mediated by the twin-cycle. We aimed to examine this notion in the Innovative Medicines Initiative Diabetes Research on Patient Stratification (IMI DIRECT) Consortium cohorts comprised of participants with normal or impaired glucose regulation (cohort 1: N ≤ 920) or with recently diagnosed type 2 diabetes (cohort 2: N ≤ 435). METHODS: We defined a structural equation model that describes the TC and fitted this within the IMI DIRECT dataset. A second model, twin-cycle plus physical activity (TC-PA), to assess the extent to which the effects of physical activity in glycaemic regulation are mediated by components in the twin-cycle, was also fitted. Beta cell function, insulin sensitivity and glycaemic control were modelled from frequently sampled 75 g OGTTs (fsOGTTs) and mixed-meal tolerance tests (MMTTs) in participants without and with diabetes, respectively. Abdominal fat distribution was assessed using MRI, and physical activity through wrist-worn triaxial accelerometry. Results are presented as standardised beta coefficients, SE and p values, respectively. RESULTS: The TC and TC-PA models showed better fit than null models (TC: χ2 = 242, p = 0.004 and χ2 = 63, p = 0.001 in cohort 1 and 2, respectively; TC-PA: χ2 = 180, p = 0.041 and χ2 = 60, p = 0.008 in cohort 1 and 2, respectively). The association of physical activity with glycaemic control was primarily mediated by variables in the liver fat cycle. CONCLUSIONS/INTERPRETATION: These analyses partially support the mechanisms proposed in the twin-cycle model and highlight mechanistic pathways through which insulin sensitivity and liver fat mediate the association between physical activity and glycaemic control.
44. Genomic and drug target evaluation of 90 cardiovascular proteins in 30,931 individuals
- Author
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Folkersen, Lasse, Gustafsson, Stefan, Wang, Qin, Hansen, Daniel Hvidberg, Hedman, Åsa K, Schork, Andrew, Page, Karen, Zhernakova, Daria V, Wu, Yang, Peters, James, Eriksson, Niclas, Bergen, Sarah E, Boutin, Thibaud S, Bretherick, Andrew D, Enroth, Stefan, Kalnapenkis, Anette, Gådin, Jesper R, Suur, Bianca E, Chen, Yan, Matic, Ljubica, Gale, Jeremy D, Lee, Julie, Zhang, Weidong, Quazi, Amira, Ala-Korpela, Mika, Choi, Seung Hoan, Claringbould, Annique, Danesh, John, Davey Smith, George, De Masi, Federico, Elmståhl, Sölve, Engström, Gunnar, Fauman, Eric, Fernandez, Celine, Franke, Lude, Franks, Paul W, Giedraitis, Vilmantas, Haley, Chris, Hamsten, Anders, Ingason, Andres, Johansson, Åsa, Joshi, Peter K, Lind, Lars, Lindgren, Cecilia M, Lubitz, Steven, Palmer, Tom, Macdonald-Dunlop, Erin, Magnusson, Martin, Melander, Olle, Michaelsson, Karl, Morris, Andrew P, Mägi, Reedik, Nagle, Michael W, Nilsson, Peter M, Nilsson, Jan, Orho-Melander, Marju, Polasek, Ozren, Prins, Bram, Pålsson, Erik, Qi, Ting, Sjögren, Marketa, Sundström, Johan, Surendran, Praveen, Võsa, Urmo, Werge, Thomas, Wernersson, Rasmus, Westra, Harm-Jan, Yang, Jian, Zhernakova, Alexandra, Ärnlöv, Johan, Fu, Jingyuan, Smith, J Gustav, Esko, Tõnu, Hayward, Caroline, Gyllensten, Ulf, Landen, Mikael, Siegbahn, Agneta, Wilson, James F, Wallentin, Lars, Butterworth, Adam S, Holmes, Michael V, Ingelsson, Erik, and Mälarstig, Anders
- Subjects
Proteome ,Receptors, CCR5 ,Receptors, CCR2 ,Quantitative Trait Loci ,Intracellular Signaling Peptides and Proteins ,Chromosome Mapping ,Genomics ,Mendelian Randomization Analysis ,Protein Serine-Threonine Kinases ,Inflammatory Bowel Diseases ,Cardiovascular System ,Interleukin-1 Receptor-Like 1 Protein ,Asthma ,Linkage Disequilibrium ,3. Good health ,Drug Delivery Systems ,Gene Knockdown Techniques ,Humans ,ATP Binding Cassette Transporter 1 ,Genome-Wide Association Study - Abstract
Circulating proteins are vital in human health and disease and are frequently used as biomarkers for clinical decision-making or as targets for pharmacological intervention. Here, we map and replicate protein quantitative trait loci (pQTL) for 90 cardiovascular proteins in over 30,000 individuals, resulting in 451 pQTLs for 85 proteins. For each protein, we further perform pathway mapping to obtain trans-pQTL gene and regulatory designations. We substantiate these regulatory findings with orthogonal evidence for trans-pQTLs using mouse knockdown experiments (ABCA1 and TRIB1) and clinical trial results (chemokine receptors CCR2 and CCR5), with consistent regulation. Finally, we evaluate known drug targets, and suggest new target candidates or repositioning opportunities using Mendelian randomization. This identifies 11 proteins with causal evidence of involvement in human disease that have not previously been targeted, including EGF, IL-16, PAPPA, SPON1, F3, ADM, CASP-8, CHI3L1, CXCL16, GDF15 and MMP-12. Taken together, these findings demonstrate the utility of large-scale mapping of the genetics of the proteome and provide a resource for future precision studies of circulating proteins in human health.
45. The role of physical activity in metabolic homeostasis before and after the onset of type 2 diabetes: an IMI DIRECT study
- Author
-
Koivula, Robert W., Atabaki-Pasdar, Naeimeh, Giordano, Giuseppe N., White, Tom, Adamski, Jerzy, Bell, Jimmy D., Beulens, Joline, Brage, Søren, Brunak, Søren, De Masi, Federico, Dermitzakis, Emmanouil T., Forgie, Ian M., Frost, Gary, Hansen, Torben, Hansen, Tue H., Hattersley, Andrew, Kokkola, Tarja, Kurbasic, Azra, Laakso, Markku, Mari, Andrea, McDonald, Timothy J., Pedersen, Oluf, Rutters, Femke, Schwenk, Jochen M., Teare, Harriet J. A., Thomas, E. Louise, Vinuela, Ana, Mahajan, Anubha, McCarthy, Mark I., Ruetten, Hartmut, Walker, Mark, Pearson, Ewan, Pavo, Imre, and Franks, Paul W.
- Subjects
Physical activity ,Structural equation modelling ,Glycaemic control ,Beta cell function ,Type 2 diabetes ,16. Peace & justice ,Insulin sensitivity ,Prediabetes ,Article ,Ectopic fat ,3. Good health - Abstract
Aims/hypothesis: It is well established that physical activity, abdominal ectopic fat and glycaemic regulation are related but the underlying structure of these relationships is unclear. The previously proposed twin-cycle hypothesis (TC) provides a mechanistic basis for impairment in glycaemic control through the interactions of substrate availability, substrate metabolism and abdominal ectopic fat accumulation. Here, we hypothesise that the effect of physical activity in glucose regulation is mediated by the twin-cycle. We aimed to examine this notion in the Innovative Medicines Initiative Diabetes Research on Patient Stratification (IMI DIRECT) Consortium cohorts comprised of participants with normal or impaired glucose regulation (cohort 1: N ≤ 920) or with recently diagnosed type 2 diabetes (cohort 2: N ≤ 435). Methods: We defined a structural equation model that describes the TC and fitted this within the IMI DIRECT dataset. A second model, twin-cycle plus physical activity (TC-PA), to assess the extent to which the effects of physical activity in glycaemic regulation are mediated by components in the twin-cycle, was also fitted. Beta cell function, insulin sensitivity and glycaemic control were modelled from frequently sampled 75 g OGTTs (fsOGTTs) and mixed-meal tolerance tests (MMTTs) in participants without and with diabetes, respectively. Abdominal fat distribution was assessed using MRI, and physical activity through wrist-worn triaxial accelerometry. Results are presented as standardised beta coefficients, SE and p values, respectively. Results: The TC and TC-PA models showed better fit than null models (TC: χ2 = 242, p = 0.004 and χ2 = 63, p = 0.001 in cohort 1 and 2, respectively; TC-PA: χ2 = 180, p = 0.041 and χ2 = 60, p = 0.008 in cohort 1 and 2, respectively). The association of physical activity with glycaemic control was primarily mediated by variables in the liver fat cycle. Conclusions/interpretation: These analyses partially support the mechanisms proposed in the twin-cycle model and highlight mechanistic pathways through which insulin sensitivity and liver fat mediate the association between physical activity and glycaemic control.
46. The role of physical activity in metabolic homeostasis before and after the onset of type 2 diabetes: an IMI DIRECT study
- Author
-
Koivula, Robert W., Atabaki-Pasdar, Naeimeh, Giordano, Giuseppe N., White, Tom, Adamski, Jerzy, Bell, Jimmy D., Beulens, Joline, Brage, Søren, Brunak, Søren, De Masi, Federico, Dermitzakis, Emmanouil T., Forgie, Ian M., Frost, Gary, Hansen, Torben, Hansen, Tue H., Hattersley, Andrew, Kokkola, Tarja, Kurbasic, Azra, Laakso, Markku, Mari, Andrea, McDonald, Timothy J., Pedersen, Oluf, Rutters, Femke, Schwenk, Jochen M., Teare, Harriet J. A., Thomas, E. Louise, Vinuela, Ana, Mahajan, Anubha, McCarthy, Mark I., Ruetten, Hartmut, Walker, Mark, Pearson, Ewan, Pavo, Imre, and Franks, Paul W.
- Subjects
Physical activity ,Structural equation modelling ,Glycaemic control ,Beta cell function ,Type 2 diabetes ,Insulin sensitivity ,Prediabetes ,Article ,Ectopic fat ,3. Good health - Abstract
Aims/hypothesis: It is well established that physical activity, abdominal ectopic fat and glycaemic regulation are related but the underlying structure of these relationships is unclear. The previously proposed twin-cycle hypothesis (TC) provides a mechanistic basis for impairment in glycaemic control through the interactions of substrate availability, substrate metabolism and abdominal ectopic fat accumulation. Here, we hypothesise that the effect of physical activity in glucose regulation is mediated by the twin-cycle. We aimed to examine this notion in the Innovative Medicines Initiative Diabetes Research on Patient Stratification (IMI DIRECT) Consortium cohorts comprised of participants with normal or impaired glucose regulation (cohort 1: N ≤ 920) or with recently diagnosed type 2 diabetes (cohort 2: N ≤ 435). Methods: We defined a structural equation model that describes the TC and fitted this within the IMI DIRECT dataset. A second model, twin-cycle plus physical activity (TC-PA), to assess the extent to which the effects of physical activity in glycaemic regulation are mediated by components in the twin-cycle, was also fitted. Beta cell function, insulin sensitivity and glycaemic control were modelled from frequently sampled 75 g OGTTs (fsOGTTs) and mixed-meal tolerance tests (MMTTs) in participants without and with diabetes, respectively. Abdominal fat distribution was assessed using MRI, and physical activity through wrist-worn triaxial accelerometry. Results are presented as standardised beta coefficients, SE and p values, respectively. Results: The TC and TC-PA models showed better fit than null models (TC: χ2 = 242, p = 0.004 and χ2 = 63, p = 0.001 in cohort 1 and 2, respectively; TC-PA: χ2 = 180, p = 0.041 and χ2 = 60, p = 0.008 in cohort 1 and 2, respectively). The association of physical activity with glycaemic control was primarily mediated by variables in the liver fat cycle. Conclusions/interpretation: These analyses partially support the mechanisms proposed in the twin-cycle model and highlight mechanistic pathways through which insulin sensitivity and liver fat mediate the association between physical activity and glycaemic control.
47. 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
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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, and Franks, Paul W.
- Subjects
Genome ,Physical activity ,Insulin secretion ,Glycaemic control ,Personalised medicine ,Type 2 diabetes ,16. Peace & justice ,Insulin sensitivity ,Prediabetes ,Article ,Ectopic fat ,3. Good health ,Diet - Abstract
Aims/hypothesis: Here, we describe the characteristics of the Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) epidemiological cohorts at baseline and follow-up examinations (18, 36 and 48 months of follow-up). Methods: From a sampling frame of 24,682 adults of European ancestry enrolled in population-based cohorts across Europe, participants at varying risk of glycaemic deterioration were identified using a risk prediction algorithm (based on age, BMI, waist circumference, use of antihypertensive medication, smoking status and parental history of type 2 diabetes) and enrolled into a prospective cohort study (n = 2127) (cohort 1, prediabetes risk). We also recruited people from clinical registries with type 2 diabetes diagnosed 6–24 months previously (n = 789) into a second cohort study (cohort 2, diabetes). Follow-up examinations took place at ~18 months (both cohorts) and at ~48 months (cohort 1) or ~36 months (cohort 2) after baseline examinations. The cohorts were studied in parallel using matched protocols across seven clinical centres in northern Europe. Results: Using ADA 2011 glycaemic categories, 33% (n = 693) of cohort 1 (prediabetes risk) had normal glucose regulation and 67% (n = 1419) had impaired glucose regulation. Seventy-six per cent of participants in cohort 1 was male. Cohort 1 participants had the following characteristics (mean ± SD) at baseline: age 62 (6.2) years; BMI 27.9 (4.0) kg/m2; fasting glucose 5.7 (0.6) mmol/l; 2 h glucose 5.9 (1.6) mmol/l. At the final follow-up examination the participants’ clinical characteristics were as follows: fasting glucose 6.0 (0.6) mmol/l; 2 h OGTT glucose 6.5 (2.0) mmol/l. In cohort 2 (diabetes), 66% (n = 517) were treated by lifestyle modification and 34% (n = 272) were treated with metformin plus lifestyle modification at enrolment. Fifty-eight per cent of participants in cohort 2 was male. Cohort 2 participants had the following characteristics at baseline: age 62 (8.1) years; BMI 30.5 (5.0) kg/m2; fasting glucose 7.2 (1.4) mmol/l; 2 h glucose 8.6 (2.8) mmol/l. At the final follow-up examination, the participants’ clinical characteristics were as follows: fasting glucose 7.9 (2.0) mmol/l; 2 h mixed-meal tolerance test glucose 9.9 (3.4) mmol/l. Conclusions/interpretation: The IMI DIRECT cohorts are intensely characterised, with a wide-variety of metabolically relevant measures assessed prospectively. We anticipate that the cohorts, made available through managed access, will provide a powerful resource for biomarker discovery, multivariate aetiological analyses and reclassification of patients for the prevention and treatment of type 2 diabetes.
48. Jolly Good.
- Author
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De Masi, Federico
- Subjects
LETTERS to the editor ,COMPUTER software - Abstract
Presents a letter to the editor in response to the article about Skype.
- Published
- 2005
49. High-resolution DNA-binding specificity analysis of yeast transcription factors.
- Author
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Cong Zhu, Byers, Kelsey J. R. P., McCord, Rachel Patton, Zhenwei Shi, Berger, Michael F., Newburger, Daniel E., Saulrieta, Katrina, Smith, Zachary, Shah, Mita V., Radhakrishnan, Mathangi, Philippakis, Anthony A., Yanhui Hu, De Masi, Federico, Pacek, Marcin, Rolfs, Andreas, Murthy, Tal, LaBaer, Joshua, and Bulyk, Martha L.
- Subjects
- *
DNA-binding proteins , *PROTEINS , *LEUCINE zippers , *IMMUNOSPECIFICITY , *YEAST , *TRANSCRIPTION factors - Abstract
Transcription factors (TFs) regulate the expression of genes through sequence-specific interactions with DNA-binding sites. However, despite recent progress in identifying in vivo TF binding sites by microarray readout of chromatin immunoprecipitation (ChIP-chip), nearly half of all known yeast TFs are of unknown DNA-binding specificities, and many additional predicted TFs remain uncharacterized. To address these gaps in our knowledge of yeast TFs and their cis regulatory sequences, we have determined high-resolution binding profiles for 89 known and predicted yeast TFs, over more than 2.3 million gapped and ungapped 8-bp sequences ("k-mers"). We report 50 new or significantly different direct DNA-binding site motifs for yeast DNA-binding proteins and motifs for eight proteins for which only a consensus sequence was previously known; in total, this corresponds to over a 50% increase in the number of yeast DNA-binding proteins with experimentally determined DNA-binding specificities. Among other novel regulators, we discovered proteins that bind the PAC (Polymerase A and C) motif (GATGAG) and regulate ribosomal RNA (rRNA) transcription and processing, core cellular processes that are constituent to ribosome biogenesis. In contrast to earlier data types, these comprehensive k-mer binding data permit us to consider the regulatory potential of genomic sequence at the individual word level. These k-mer data allowed us to reannotate in vivo TF binding targets as direct or indirect and to examine TFs' potential effects on gene expression in ⇔/41700 environmental and cellular conditions. These approaches could be adapted to identify TFs and cis regulatory elements in higher eukaryotes. [ABSTRACT FROM AUTHOR]
- Published
- 2009
- Full Text
- View/download PDF
50. Processes Underlying Glycemic Deterioration in Type 2 Diabetes: An IMI DIRECT Study.
- Author
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Bizzotto R, Jennison C, Jones AG, Kurbasic A, Tura A, Kennedy G, Bell JD, Thomas EL, Frost G, Eriksen R, Koivula RW, Brage S, Kaye J, Hattersley AT, Heggie A, McEvoy D, 't Hart LM, Beulens JW, Elders P, Musholt PB, Ridderstråle M, Hansen TH, Allin KH, Hansen T, Vestergaard H, Lundgaard AT, Thomsen HS, De Masi F, Tsirigos KD, Brunak S, Viñuela A, Mahajan A, McDonald TJ, Kokkola T, Forgie IM, Giordano GN, Pavo I, Ruetten H, Dermitzakis E, McCarthy MI, Pedersen O, Schwenk JM, Adamski J, Franks PW, Walker M, Pearson ER, and Mari A
- Subjects
- Blood Glucose, Cholesterol, HDL, Humans, Insulin, Diabetes Mellitus, Type 2, Insulin Resistance, Insulin-Secreting Cells
- Abstract
Objective: We investigated the processes underlying glycemic deterioration in type 2 diabetes (T2D)., Research Design and Methods: A total of 732 recently diagnosed patients with T2D from the Innovative Medicines Initiative Diabetes Research on Patient Stratification (IMI DIRECT) study were extensively phenotyped over 3 years, including measures of insulin sensitivity (OGIS), β-cell glucose sensitivity (GS), and insulin clearance (CLIm) from mixed meal tests, liver enzymes, lipid profiles, and baseline regional fat from MRI. The associations between the longitudinal metabolic patterns and HbA
1c deterioration, adjusted for changes in BMI and in diabetes medications, were assessed via stepwise multivariable linear and logistic regression., Results: Faster HbA1c progression was independently associated with faster deterioration of OGIS and GS and increasing CLIm; visceral or liver fat, HDL-cholesterol, and triglycerides had further independent, though weaker, roles ( R2 = 0.38). A subgroup of patients with a markedly higher progression rate (fast progressors) was clearly distinguishable considering these variables only (discrimination capacity from area under the receiver operating characteristic = 0.94). The proportion of fast progressors was reduced from 56% to 8-10% in subgroups in which only one trait among OGIS, GS, and CLIm was relatively stable (odds ratios 0.07-0.09). T2D polygenic risk score and baseline pancreatic fat, glucagon-like peptide 1, glucagon, diet, and physical activity did not show an independent role., Conclusions: Deteriorating insulin sensitivity and β-cell function, increasing insulin clearance, high visceral or liver fat, and worsening of the lipid profile are the crucial factors mediating glycemic deterioration of patients with T2D in the initial phase of the disease. Stabilization of a single trait among insulin sensitivity, β-cell function, and insulin clearance may be relevant to prevent progression., (© 2020 by the American Diabetes Association.)- Published
- 2021
- Full Text
- View/download PDF
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