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2. Elevated plasma complement factor H related 5 protein is associated with venous thromboembolism

Author

Iglesias, Maria Jesus, Sanchez-Rivera, Laura, Ibrahim-Kosta, Manal, Naudin, Clément, Munsch, Gaëlle, Goumidi, Louisa, Farm, Maria, Smith, Philip M., Thibord, Florian, Kral-Pointner, Julia Barbara, Hong, Mun-Gwan, Suchon, Pierre, Germain, Marine, Schrottmaier, Waltraud, Dusart, Philip, Boland, Anne, Kotol, David, Edfors, Fredrik, Koprulu, Mine, Pietzner, Maik, Langenberg, Claudia, Damrauer, Scott M., Johnson, Andrew D., Klarin, Derek M., Smith, Nicholas L., Smadja, David M., Holmström, Margareta, Magnusson, Maria, Silveira, Angela, Uhlén, Mathias, Renné, Thomas, Martinez-Perez, Angel, Emmerich, Joseph, Deleuze, Jean-Francois, Antovic, Jovan, Soria Fernandez, Jose Manuel, Assinger, Alice, Schwenk, Jochen M., Souto Andres, Joan Carles, Morange, Pierre-Emmanuel, Butler, Lynn Marie, Trégouët, David-Alexandre, and Odeberg, Jacob

Published
2023
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3. Author Correction: Elevated plasma complement factor H related 5 protein is associated with venous thromboembolism

Author

Iglesias, Maria Jesus, Sanchez-Rivera, Laura, Ibrahim-Kosta, Manal, Naudin, Clément, Munsch, Gaëlle, Goumidi, Louisa, Farm, Maria, Smith, Philip M., Thibord, Florian, Kral-Pointner, Julia Barbara, Hong, Mun-Gwan, Suchon, Pierre, Germain, Marine, Schrottmaier, Waltraud, Dusart, Philip, Boland, Anne, Kotol, David, Edfors, Fredrik, Koprulu, Mine, Pietzner, Maik, Langenberg, Claudia, Damrauer, Scott M., Johnson, Andrew D., Klarin, Derek M., Smith, Nicholas L., Smadja, David M., Holmström, Margareta, Magnusson, Maria, Silveira, Angela, Uhlén, Mathias, Renné, Thomas, Martinez-Perez, Angel, Emmerich, Joseph, Deleuze, Jean-Francois, Antovic, Jovan, Soria Fernandez, Jose Manuel, Assinger, Alice, Schwenk, Jochen M., Souto Andres, Joan Carles, Morange, Pierre-Emmanuel, Butler, Lynn Marie, Trégouët, David-Alexandre, and Odeberg, Jacob

Published
2023
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5. Genetic analysis of blood molecular phenotypes reveals common properties in the regulatory networks affecting complex traits

Author

Brown, Andrew A., Fernandez-Tajes, Juan J., Hong, Mun-gwan, Brorsson, Caroline A., Koivula, Robert W., Davtian, David, Dupuis, Théo, Sartori, Ambra, Michalettou, Theodora-Dafni, Forgie, Ian M., Adam, Jonathan, Allin, Kristine H., Caiazzo, Robert, Cederberg, Henna, De Masi, Federico, Elders, Petra J. M., Giordano, Giuseppe N., Haid, Mark, Hansen, Torben, Hansen, Tue H., Hattersley, Andrew T., Heggie, Alison J., Howald, Cédric, Jones, Angus G., Kokkola, Tarja, Laakso, Markku, Mahajan, Anubha, Mari, Andrea, McDonald, Timothy J., McEvoy, Donna, Mourby, Miranda, Musholt, Petra B., Nilsson, Birgitte, Pattou, Francois, Penet, Deborah, Raverdy, Violeta, Ridderstråle, Martin, Romano, Luciana, Rutters, Femke, Sharma, Sapna, Teare, Harriet, ‘t Hart, Leen, Tsirigos, Konstantinos D., Vangipurapu, Jagadish, Vestergaard, Henrik, Brunak, Søren, Franks, Paul W., Frost, Gary, Grallert, Harald, Jablonka, Bernd, McCarthy, Mark I., Pavo, Imre, Pedersen, Oluf, Ruetten, Hartmut, Walker, Mark, Adamski, Jerzy, Schwenk, Jochen M., Pearson, Ewan R., Dermitzakis, Emmanouil T., and Viñuela, Ana

Published
2023
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7. A survey of ficolin-3 activity in Systemic Lupus Erythematosus reveals a link to hematological disease manifestations and autoantibody profile

Author

Lindelöf, Linnea, Rantapää-Dahlqvist, Solbritt, Lundtoft, Christian, Sandling, Johanna K., Leonard, Dag, Sayadi, Ahmed, Rönnblom, Lars, Enocsson, Helena, Sjöwall, Christopher, Jönsen, Andreas, Bengtsson, Anders A., Hong, Mun-Gwan, Diaz-Gallo, Lina-Marcela, Bianchi, Matteo, Kozyrev, Sergey V., Lindblad-Toh, Kerstin, Nilsson Ekdahl, Kristina, Nilsson, Bo, Gunnarsson, Iva, Svenungsson, Elisabet, and Eriksson, Oskar

Published
2024
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8. Discovery of drug–omics associations in type 2 diabetes with generative deep-learning models

Author

Allesøe, Rosa Lundbye, Lundgaard, Agnete Troen, Hernández Medina, Ricardo, Aguayo-Orozco, Alejandro, Johansen, Joachim, Nissen, Jakob Nybo, Brorsson, Caroline, Mazzoni, Gianluca, Niu, Lili, Biel, Jorge Hernansanz, Leal Rodríguez, Cristina, Brasas, Valentas, Webel, Henry, Benros, Michael Eriksen, Pedersen, Anders Gorm, Chmura, Piotr Jaroslaw, Jacobsen, Ulrik Plesner, Mari, Andrea, Koivula, Robert, Mahajan, Anubha, Vinuela, Ana, Tajes, Juan Fernandez, Sharma, Sapna, Haid, Mark, Hong, Mun-Gwan, Musholt, Petra B., De Masi, Federico, Vogt, Josef, Pedersen, Helle Krogh, Gudmundsdottir, Valborg, Jones, Angus, Kennedy, Gwen, Bell, Jimmy, Thomas, E. Louise, Frost, Gary, Thomsen, Henrik, Hansen, Elizaveta, Hansen, Tue Haldor, Vestergaard, Henrik, Muilwijk, Mirthe, Blom, Marieke T., ‘t Hart, Leen M., Pattou, Francois, Raverdy, Violeta, Brage, Soren, Kokkola, Tarja, Heggie, Alison, McEvoy, Donna, Mourby, Miranda, Kaye, Jane, Hattersley, Andrew, McDonald, Timothy, Ridderstråle, Martin, Walker, Mark, Forgie, Ian, Giordano, Giuseppe N., Pavo, Imre, Ruetten, Hartmut, Pedersen, Oluf, Hansen, Torben, Dermitzakis, Emmanouil, Franks, Paul W., Schwenk, Jochen M., Adamski, Jerzy, McCarthy, Mark I., Pearson, Ewan, Banasik, Karina, Rasmussen, Simon, and Brunak, Søren

Published
2023
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9. Pharmacogenomics of GLP-1 receptor agonists: a genome-wide analysis of observational data and large randomised controlled trials

Author

't Hart, L.M., Abdalla, M., Adam, J., Adamski, J., Adragni, K., Allin, K.H., Arumugam, M., Atabaki Pasdar, N., Baltauss, T., Banasik, K.B., Baum, P., Bell, J.D., Bergstrom, M., Beulens, J.W., Bianzano, S., Bizzotto, R., Bonneford, A., Brorsson, C.A.B., Brown, A.A., Brunak, S.B., Cabrelli, L., Caiazzo, R., Canouil, M., Dale, M., Davtian, D., Dawed, A.Y., De Masi, F.M., de Preville, N., Dekkers, K.F., Dermitzakis, E.T., Deshmukh, H.A., Dings, C., Donnelly, L., Dutta, A., Ehrhardt, B., Elders, P.J.M., Engel Thomas, C.E.T., Engelbrechtsen, L., Eriksen, R.G., Eriksen, R.E., Fan, Y., Fernandez, J., Ferrer, J., Fitipaldi, H., Forgie, I.M., Forman, A., Franks, P.W., Frau, F., Fritsche, A., Froguel, P., Frost, G., Gassenhuber, J., Giordano, G.N., Giorgino, T., Gough, S., Graefe-Mody, U., Grallert, H., Grempler, R., Groeneveld, L., Groop, L., Gudmundsdóttir, V.G., Gupta, R.G., Haid, M., Hansen, T., Hansen, T.H., Hattersley, A.T., Haussler, R.S., Heggie, A.J., Hennige, A.M., Hill, A.V., Holl, R.W., Hong, M.-G., Hudson, M., Jablonka, B., Jennison, C., Jiao, J., Johansen, J.J., Jones, A.G., Jonsson, A., Karaderi, T.K., Kaye, J., Klintenberg, M., Koivula, R.W., Kokkola, T., Koopman, A.D.M., Kurbasic, A, Kuulasmaa, T., Laakso, M., Lehr, T., Loftus, H., Lundbye Allesøe, R.L.A, Mahajan, A., Mari, A., Mazzoni, G.M., McCarthy, M.I., McDonald, T.J., McEvoy, D., McRobert, N., McVittie, I., Mourby, M., Musholt, P., Mutie, P, Nice, R., Nicolay, C., Nielsen, A.M.N., Nilsson, B.N., Palmer, C.N., Pattou, F., Pavo, I., Pearson, E.R., Pedersen, O., Pedersen, H.K.P., Perry, M.H., Pomares-Millan, H., Ramisch, A., Rasmussen, S.R., Raverdi, V., Ridderstrale, M., Robertson, N., Roderick, R.C., Rodriquez, M., Ruetten, H., Rutters, F., Sackett, W., Scherer, N., Schwenk, J.M., Shah, N., Sharma, S., Sihinevich, I., Sondertoft, N.B., Staerfeldt, H., Steckel-Hamann, B., Teare, H., Thomas, M.K., Thomas, E.L., Thomsen, H.S., Thorand, B., Thorne, C.E., Tillner, J., Troen Lundgaard, A.T.L., Troll, M., Tsirigos, K.D.T., Tura, A., Uhlen, M., van Leeuwen, N., van Oort, S., Verkindt, H., Vestergaard, H., Viñuela, A., Vogt, J.K, Wad Sackett, P.W.S, Wake, D., Walker, M., Wesolowska-Andersen, A., Whitcher, B., White, M.W., Wu, H., Dawed, Adem Y, Mari, Andrea, Brown, Andrew, McDonald, Timothy J, Li, Lin, Wang, Shuaicheng, Hong, Mun-Gwan, Sharma, Sapna, Robertson, Neil R, Mahajan, Anubha, Wang, Xuan, Walker, Mark, Gough, Stephen, Hart, Leen M ‘t, Zhou, Kaixin, Forgie, Ian, Ruetten, Hartmut, Pavo, Imre, Bhatnagar, Pallav, Jones, Angus G, and Pearson, Ewan R

Published
2023
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10. The Effect of Metformin Treatment on the Circulating Proteome

Author

Connolly, Benjamin W, primary, McCreight, Laura, additional, Slieker, Roderick, additional, Bedair, Khaled F, additional, Donnelly, Louise, additional, de Klerk, Juliette A, additional, Beulens, Joline WJ, additional, Elders, Petra M, additional, Bergstrom, Goran, additional, Hong, Mun-Gwan, additional, Koivula, Robert w, additional, Franks, Paul W, additional, 't Hart, Leen, additional, Schwenk, Jochen, additional, Gummesson, Anders, additional, and Pearson, Ewan R, additional

Published
2024
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11. Four groups of type 2 diabetes contribute to the etiological and clinical heterogeneity in newly diagnosed individuals: An IMI DIRECT study

Author

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

Published
2022
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12. Author Correction: Discovery of drug–omics associations in type 2 diabetes with generative deep-learning models

Author

Allesøe, Rosa Lundbye, Lundgaard, Agnete Troen, Hernández Medina, Ricardo, Aguayo-Orozco, Alejandro, Johansen, Joachim, Nissen, Jakob Nybo, Brorsson, Caroline, Mazzoni, Gianluca, Niu, Lili, Biel, Jorge Hernansanz, Leal Rodríguez, Cristina, Brasas, Valentas, Webel, Henry, Benros, Michael Eriksen, Pedersen, Anders Gorm, Chmura, Piotr Jaroslaw, Jacobsen, Ulrik Plesner, Mari, Andrea, Koivula, Robert, Mahajan, Anubha, Vinuela, Ana, Tajes, Juan Fernandez, Sharma, Sapna, Haid, Mark, Hong, Mun-Gwan, Musholt, Petra B., De Masi, Federico, Vogt, Josef, Pedersen, Helle Krogh, Gudmundsdottir, Valborg, Jones, Angus, Kennedy, Gwen, Bell, Jimmy, Thomas, E. Louise, Frost, Gary, Thomsen, Henrik, Hansen, Elizaveta, Hansen, Tue Haldor, Vestergaard, Henrik, Muilwijk, Mirthe, Blom, Marieke T., ‘t Hart, Leen M., Pattou, Francois, Raverdy, Violeta, Brage, Soren, Kokkola, Tarja, Heggie, Alison, McEvoy, Donna, Mourby, Miranda, Kaye, Jane, Hattersley, Andrew, McDonald, Timothy, Ridderstråle, Martin, Walker, Mark, Forgie, Ian, Giordano, Giuseppe N., Pavo, Imre, Ruetten, Hartmut, Pedersen, Oluf, Hansen, Torben, Dermitzakis, Emmanouil, Franks, Paul W., Schwenk, Jochen M., Adamski, Jerzy, McCarthy, Mark I., Pearson, Ewan, Banasik, Karina, Rasmussen, Simon, and Brunak, Søren

Published
2023
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15. Dissecting the interplay between ageing, sex and body mass index on a molecular level

Author

Michalettou, Theodora Dafni, Hong, Mun-Gwan, Fernandez, Juan, Sharma, Sapna, Brorsson, Caroline, Koivula, Robert, Adamski, Jerzy, Brunak, Soren, Dermitzakis, Emmanouil, Franks, Paul, McCarthy, Mark, Pearson, Ewan, Schwenk, Jochen M., Walker, Mark, Brown, Andrew, Vinuela, Ana, Michalettou, Theodora Dafni, Hong, Mun-Gwan, Fernandez, Juan, Sharma, Sapna, Brorsson, Caroline, Koivula, Robert, Adamski, Jerzy, Brunak, Soren, Dermitzakis, Emmanouil, Franks, Paul, McCarthy, Mark, Pearson, Ewan, Schwenk, Jochen M., Walker, Mark, Brown, Andrew, and Vinuela, Ana

Abstract

QC 20240301

Published
2024

17. Multianalyte serology in home-sampled blood enables an unbiased assessment of the immune response against SARS-CoV-2

Author

Roxhed, Niclas, Bendes, Annika, Dale, Matilda, Mattsson, Cecilia, Hanke, Leo, Dodig-Crnković, Tea, Christian, Murray, Meineke, Birthe, Elsässer, Simon, Andréll, Juni, Havervall, Sebastian, Thålin, Charlotte, Eklund, Carina, Dillner, Joakim, Beck, Olof, Thomas, Cecilia E., McInerney, Gerald, Hong, Mun-Gwan, Murrell, Ben, Fredolini, Claudia, and Schwenk, Jochen M.

Published
2021
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18. Elevated circulating follistatin associates with an increased risk of type 2 diabetes

Author

Wu, Chuanyan, Borné, Yan, Gao, Rui, López Rodriguez, Maykel, Roell, William C., Wilson, Jonathan M., Regmi, Ajit, Luan, Cheng, Aly, Dina Mansour, Peter, Andreas, Machann, Jürgen, Staiger, Harald, Fritsche, Andreas, Birkenfeld, Andreas L., Tao, Rongya, Wagner, Robert, Canouil, Mickaël, Hong, Mun-Gwan, Schwenk, Jochen M., Ahlqvist, Emma, Kaikkonen, Minna U., Nilsson, Peter, Shore, Angela C., Khan, Faisel, Natali, Andrea, Melander, Olle, Orho-Melander, Marju, Nilsson, Jan, Häring, Hans-Ulrich, Renström, Erik, Wollheim, Claes B., Engström, Gunnar, Weng, Jianping, Pearson, Ewan R., Franks, Paul W., White, Morris F., Duffin, Kevin L., Vaag, Allan Arthur, Laakso, Markku, Stefan, Norbert, Groop, Leif, and De Marinis, Yang

Published
2021
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20. 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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22. Novel Multiomics Profiling of Human Carotid Atherosclerotic Plaques and Plasma Reveals Biliverdin Reductase B as a Marker of Intraplaque Hemorrhage

Author

Matic, Ljubica Perisic, Jesus Iglesias, Maria, Vesterlund, Mattias, Lengquist, Mariette, Hong, Mun-Gwan, Saieed, Shanga, Sanchez-Rivera, Laura, Berg, Martin, Razuvaev, Anton, Kronqvist, Malin, Lund, Kent, Caidahl, Kenneth, Gillgren, Peter, Pontén, Fredrik, Uhlén, Mathias, Schwenk, Jochen M., Hansson, Göran K., Paulsson-Berne, Gabrielle, Fagman, Erika, Roy, Joy, Hultgren, Rebecka, Bergström, Göran, Lehtiö, Janne, Odeberg, Jacob, and Hedin, Ulf

Published
2018
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23. Whole blood co-expression modules associate with metabolic traits and type 2 diabetes: an IMI-DIRECT study

Author

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

Published
2020
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25. PDGFB, a new candidate plasma biomarker for venous thromboembolism: results from the VEREMA affinity proteomics study

Author

Bruzelius, Maria, Iglesias, Maria Jesus, Hong, Mun-Gwan, Sanchez-Rivera, Laura, Gyorgy, Beata, Souto, Juan Carlos, Frånberg, Mattias, Fredolini, Claudia, Strawbridge, Rona J., Holmström, Margareta, Hamsten, Anders, Uhlén, Mathias, Silveira, Angela, Soria, Jose Manuel, Smadja, David M., Butler, Lynn M., Schwenk, Jochen M., Morange, Pierre-Emmanuel, Trégouët, David-Alexandre, and Odeberg, Jacob

Published
2016
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28. Soluble CD14 and Osteoprotegerin Associate with Ankle-Brachial Index as a Measure of Arterial Stiffness in Patients with Mild-to-Moderate Chronic Kidney Disease in a Five-Year Prospective Study

Author

Sendic, Senka, Mansouri, Ladan, Hong, Mun-Gwan, Schwenk, Jochen M., Eriksson, Maria J., Hylander, Britta, Lundahl, Joachim, Jacobson, Stefan H., Sendic, Senka, Mansouri, Ladan, Hong, Mun-Gwan, Schwenk, Jochen M., Eriksson, Maria J., Hylander, Britta, Lundahl, Joachim, and Jacobson, Stefan H.

Abstract

Introduction: Vascular lesions and arterial stiffness appear at early stages of chronic kidney disease (CKD) and follow an accelerated course with disease progression, contributing to high cardiovascular mortality. There are limited prospective data on mechanisms contributing to progression of arterial stiffness in mild-to-moderate CKD (stages 2-3). Methods: We applied an affinity proteomics approach to identify candidates of circulating biomarkers with potential impact on vascular lesions in CKD and selected soluble cluster of differentiation 14 (sCD14), angiogenin (ANG), and osteoprotegerin (OPG) for further analysis. We studied their association with ankle-brachial index (ABI) and carotid intima-media thickness, as measures of arteriosclerosis and atherosclerosis, respectively, in 48 patients with CKD stages 2-3, who were prospectively followed and intensively treated for 5 years, and 44 healthy controls. Results: Concentrations of sCD14 (p < 0.001), ANG (p < 0.001), and OPG (p < 0.05) were higher in patients with CKD 2-3 at baseline, and sCD14 (p < 0.001) and ANG (p < 0.001) remained elevated in CKD patients at follow-up. There were positive correlations between ABI and sCD14 levels (r = 0.36, p = 0.01) and between ABI and OPG (r = 0.31, p = 0.03) at 5 years. The changes in sCD14 during follow-up correlated to changes in ABI from baseline to 5 years (r = 0.41, p = 0.004). Conclusion: Elevated levels of circulating sCD14 and OPG in patients with CKD 2-3 were significantly associated with ABI, a measure of arterial stiffness. An increase in sCD14 over time in CKD 2-3 patients was associated with a corresponding increase in ABI. Further studies are needed to examine if early intensive multifactorial medication to align with international treatment targets may influence cardiovascular outcomes., QC 20240115

Published
2023
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29. Identification of shared molecular signatures of ageing and metabolic diseases using multi-omic data

Author

Michalettou, Theodora Dafni, Hong, Mun-Gwan, Fernandez, Juan, Sharma, Sapna, Brorsson, Caroline Anna, Koivula, Robert, Adamski, Jerzy, Brunak, Soren, Dermitzakis, Emmanouil, Franks, Paul, McCarthy, Mark, Pearson, Ewan, Schwenk, Jochen, Walker, Mark, Brown, Andrew, Vinuela, Ana, Michalettou, Theodora Dafni, Hong, Mun-Gwan, Fernandez, Juan, Sharma, Sapna, Brorsson, Caroline Anna, Koivula, Robert, Adamski, Jerzy, Brunak, Soren, Dermitzakis, Emmanouil, Franks, Paul, McCarthy, Mark, Pearson, Ewan, Schwenk, Jochen, Walker, Mark, Brown, Andrew, and Vinuela, Ana

Abstract

QC 20231128

Published
2023

30. Discovery of Type 2 Diabetes genes using an accessible tissue

Author

Davtian, David, Schwenk, Jochen M., McCarthy, Mark, Mahajan, Anubha, Hong, Mun-Gwan, Dermitzakis, Emmanouil, Im, Hae Kyung, Pearson, Ewan, Vinuela, Ana, Brown, Andrew, Davtian, David, Schwenk, Jochen M., McCarthy, Mark, Mahajan, Anubha, Hong, Mun-Gwan, Dermitzakis, Emmanouil, Im, Hae Kyung, Pearson, Ewan, Vinuela, Ana, and Brown, Andrew

Abstract

QC 20231121

Published
2023

31. Pharmacogenomics of GLP-1 receptor agonists : a genome-wide analysis of observational data and large randomised controlled trials

Author

Dawed, Adem Y., Mari, Andrea, Brown, Andrew, McDonald, Timothy J., Li, Lin, Wang, Shuaicheng, Hong, Mun-Gwan, Sharma, Sapna, Robertson, Neil R., Mahajan, Anubha, Wang, Xuan, Walker, Mark, Gough, Stephen, Hart, Leen M. 't, Zhou, Kaixin, Forgie, Ian, Ruetten, Hartmut, Pavo, Imre, Bhatnagar, Pallav, Jones, Angus G., Pearson, Ewan R., DIRECT consortium, for the D. I. R. E. C. T. consortium, Dawed, Adem Y., Mari, Andrea, Brown, Andrew, McDonald, Timothy J., Li, Lin, Wang, Shuaicheng, Hong, Mun-Gwan, Sharma, Sapna, Robertson, Neil R., Mahajan, Anubha, Wang, Xuan, Walker, Mark, Gough, Stephen, Hart, Leen M. 't, Zhou, Kaixin, Forgie, Ian, Ruetten, Hartmut, Pavo, Imre, Bhatnagar, Pallav, Jones, Angus G., Pearson, Ewan R., and DIRECT consortium, for the D. I. R. E. C. T. consortium

Abstract

Background: In the treatment of type 2 diabetes, GLP-1 receptor agonists lower blood glucose concentrations, body weight, and have cardiovascular benefits. The efficacy and side effects of GLP-1 receptor agonists vary between people. Human pharmacogenomic studies of this inter-individual variation can provide both biological insight into drug action and provide biomarkers to inform clinical decision making. We therefore aimed to identify genetic variants associated with glycaemic response to GLP-1 receptor agonist treatment. Methods: In this genome-wide analysis we included adults (aged >= 18 years) with type 2 diabetes treated with GLP-1 receptor agonists with baseline HbA1c of 7% or more (53 mmol/mol) from four prospective observational cohorts (DIRECT, PRIBA, PROMASTER, and GoDARTS) and two randomised clinical trials (HARMONY phase 3 and AWARD). The primary endpoint was HbA1c reduction at 6 months after starting GLP-1 receptor agonists. We evaluated variants in GLP1R, then did a genome-wide association study and gene-based burden tests. Findings: 4571 adults were included in our analysis, of these, 3339 (73%) were White European, 449 (10%) Hispanic, 312 (7%) American Indian or Alaskan Native, and 471 (10%) were other, and around 2140 (47%) of the participants were women. Variation in HbA1c reduction with GLP-1 receptor agonists treatment was associated with rs6923761G -> A (Gly168Ser) in the GLP1R (0.08% [95% CI 0.04-0.12] or 0.9 mmol/mol lower reduction in HbA1c per serine, p=6.0 x 10(-5)) and low frequency variants in ARRB1 (optimal sequence kernel association test p=6.7 x 10(-8)), largely driven by rs140226575G -> A (Thr370Met; 0.25% [SE 0.06] or 2.7 mmol/mol [SE 0.7] greater HbA1c reduction per methionine, p=5.2 x 10(-6)). A similar effect size for the ARRB1 Thr370Met was seen in Hispanic and American Indian or Alaska Native populations who have a higher frequency of this variant (6-11%) than in White European populations. Combining these two genes i

Published
2023
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32. Characterization of the Mitochondrial Genetic Landscape in Abdominal Aortic Aneurysm

Author

Vats, Sakshi, Sundquist, Kristina, Li, Yanni, Wang, Xiao, Hong, Mun-Gwan, Sundquist, Jan, Zarrouk, Moncef, Gottsäter, Anders, Memon, Ashfaque A., Vats, Sakshi, Sundquist, Kristina, Li, Yanni, Wang, Xiao, Hong, Mun-Gwan, Sundquist, Jan, Zarrouk, Moncef, Gottsäter, Anders, and Memon, Ashfaque A.

Abstract

Background: Abdominal aortic aneurysm (AAA) is a vascular disease with a mortality rate of >80% if ruptured. Mitochondrial dysfunction has been previously implicated in AAA pathogenesis. In this study, we aimed to characterize the mitochondrial genetic landscape in AAA. Methods and Results: Whole mitochondrial genome sequencing and bioinformatics analysis were performed in comorbidity matched 48 cases without AAA and 48 cases with AAA, objectively diagnosed, and selected from a cohort of 65‐year‐old men recruited for a screening program. We identified differential mutational landscapes in men with and without AAA, with errors in mitochondrial DNA replication or repair as potential sources. Heteroplasmic insertions and overall heteroplasmy of structural rearrangements were significantly elevated in AAA cases. Three heteroplasmic variants were associated with risk factors of AAA: leukocyte concentration, plasma glucose, and cholesterol levels, respectively. Interestingly, mutations were more prevalent in regulatory part of the mitochondria, the displacement loop region, in AAA as compared with controls (P value <0.05), especially in the conserved and critical mitochondrial extended termination‐associated sequence region. Moreover, we report a novel 24 bp mitochondrial DNA duplication present exclusively in cases with AAA (4%) and 75% of the unmatched AAA biopsies. Finally, the haplogroup cluster JTU was overrepresented in AAA and significantly associated with a positive family history of AAA (odds ratio, 2.9 [95% CI, 1.1–8.1]). Conclusions: This is the first study investigating the mitochondrial genome in AAA, where important genetic alterations and haplogroups associated with AAA and clinical risk factors were identified. Our findings have the potential to fill in gaps in the missing genetic information on AAA.

Published
2023
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33. Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models:[with Author Correction]

Author

Allesøe, Rosa Lundbye, Lundgaard, Agnete Troen, Hernández Medina, Ricardo, Aguayo-Orozco, Alejandro, Johansen, Joachim, Nissen, Jakob Nybo, Brorsson, Caroline, Mazzoni, Gianluca, Niu, Lili, Biel, Jorge Hernansanz, Brasas, Valentas, Webel, Henry, Benros, Michael Eriksen, Pedersen, Anders Gorm, Chmura, Piotr Jaroslaw, Jacobsen, Ulrik Plesner, Mari, Andrea, Koivula, Robert, Mahajan, Anubha, Vinuela, Ana, Tajes, Juan Fernandez, Sharma, Sapna, Haid, Mark, Hong, Mun-Gwan, Musholt, Petra B, De Masi, Federico, Vogt, Josef, Pedersen, Helle Krogh, Gudmundsdottir, Valborg, Jones, Angus, Kennedy, Gwen, Bell, Jimmy, Thomas, E Louise, Frost, Gary, Thomsen, Henrik, Hansen, Elizaveta, Hansen, Tue Haldor, Vestergaard, Henrik, Muilwijk, Mirthe, Blom, Marieke T, 't Hart, Leen M, Pattou, Francois, Raverdy, Violeta, Brage, Soren, Ridderstråle, Martin, Pedersen, Oluf, Hansen, Torben, Banasik, Karina, Rasmussen, Simon, Brunak, Søren, Allesøe, Rosa Lundbye, Lundgaard, Agnete Troen, Hernández Medina, Ricardo, Aguayo-Orozco, Alejandro, Johansen, Joachim, Nissen, Jakob Nybo, Brorsson, Caroline, Mazzoni, Gianluca, Niu, Lili, Biel, Jorge Hernansanz, Brasas, Valentas, Webel, Henry, Benros, Michael Eriksen, Pedersen, Anders Gorm, Chmura, Piotr Jaroslaw, Jacobsen, Ulrik Plesner, Mari, Andrea, Koivula, Robert, Mahajan, Anubha, Vinuela, Ana, Tajes, Juan Fernandez, Sharma, Sapna, Haid, Mark, Hong, Mun-Gwan, Musholt, Petra B, De Masi, Federico, Vogt, Josef, Pedersen, Helle Krogh, Gudmundsdottir, Valborg, Jones, Angus, Kennedy, Gwen, Bell, Jimmy, Thomas, E Louise, Frost, Gary, Thomsen, Henrik, Hansen, Elizaveta, Hansen, Tue Haldor, Vestergaard, Henrik, Muilwijk, Mirthe, Blom, Marieke T, 't Hart, Leen M, Pattou, Francois, Raverdy, Violeta, Brage, Soren, Ridderstråle, Martin, Pedersen, Oluf, Hansen, Torben, Banasik, Karina, Rasmussen, Simon, and Brunak, Søren

Abstract

The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug-omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug-drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities.

Published
2023

35. Pharmacogenomics of GLP-1 receptor agonists: a genome-wide analysis of observational data and large randomised controlled trials

Author

Dawed, Adem Y, primary, Mari, Andrea, additional, Brown, Andrew, additional, McDonald, Timothy J, additional, Li, Lin, additional, Wang, Shuaicheng, additional, Hong, Mun-Gwan, additional, Sharma, Sapna, additional, Robertson, Neil R, additional, Mahajan, Anubha, additional, Wang, Xuan, additional, Walker, Mark, additional, Gough, Stephen, additional, Hart, Leen M ‘t, additional, Zhou, Kaixin, additional, Forgie, Ian, additional, Ruetten, Hartmut, additional, Pavo, Imre, additional, Bhatnagar, Pallav, additional, Jones, Angus G, additional, Pearson, Ewan R, additional, 't Hart, L.M., additional, Abdalla, M., additional, Adam, J., additional, Adamski, J., additional, Adragni, K., additional, Allin, K.H., additional, Arumugam, M., additional, Atabaki Pasdar, N., additional, Baltauss, T., additional, Banasik, K.B., additional, Baum, P., additional, Bell, J.D., additional, Bergstrom, M., additional, Beulens, J.W., additional, Bianzano, S., additional, Bizzotto, R., additional, Bonneford, A., additional, Brorsson, C.A.B., additional, Brown, A.A., additional, Brunak, S.B., additional, Cabrelli, L., additional, Caiazzo, R., additional, Canouil, M., additional, Dale, M., additional, Davtian, D., additional, Dawed, A.Y., additional, De Masi, F.M., additional, de Preville, N., additional, Dekkers, K.F., additional, Dermitzakis, E.T., additional, Deshmukh, H.A., additional, Dings, C., additional, Donnelly, L., additional, Dutta, A., additional, Ehrhardt, B., additional, Elders, P.J.M., additional, Engel Thomas, C.E.T., additional, Engelbrechtsen, L., additional, Eriksen, R.G., additional, Eriksen, R.E., additional, Fan, Y., additional, Fernandez, J., additional, Ferrer, J., additional, Fitipaldi, H., additional, Forgie, I.M., additional, Forman, A., additional, Franks, P.W., additional, Frau, F., additional, Fritsche, A., additional, Froguel, P., additional, Frost, G., additional, Gassenhuber, J., additional, Giordano, G.N., additional, Giorgino, T., additional, Gough, S., additional, Graefe-Mody, U., additional, Grallert, H., additional, Grempler, R., additional, Groeneveld, L., additional, Groop, L., additional, Gudmundsdóttir, V.G., additional, Gupta, R.G., additional, Haid, M., additional, Hansen, T., additional, Hansen, T.H., additional, Hattersley, A.T., additional, Haussler, R.S., additional, Heggie, A.J., additional, Hennige, A.M., additional, Hill, A.V., additional, Holl, R.W., additional, Hong, M.-G., additional, Hudson, M., additional, Jablonka, B., additional, Jennison, C., additional, Jiao, J., additional, Johansen, J.J., additional, Jones, A.G., additional, Jonsson, A., additional, Karaderi, T.K., additional, Kaye, J., additional, Klintenberg, M., additional, Koivula, R.W., additional, Kokkola, T., additional, Koopman, A.D.M., additional, Kurbasic, A, additional, Kuulasmaa, T., additional, Laakso, M., additional, Lehr, T., additional, Loftus, H., additional, Lundbye Allesøe, R.L.A, additional, Mahajan, A., additional, Mari, A., additional, Mazzoni, G.M., additional, McCarthy, M.I., additional, McDonald, T.J., additional, McEvoy, D., additional, McRobert, N., additional, McVittie, I., additional, Mourby, M., additional, Musholt, P., additional, Mutie, P, additional, Nice, R., additional, Nicolay, C., additional, Nielsen, A.M.N., additional, Nilsson, B.N., additional, Palmer, C.N., additional, Pattou, F., additional, Pavo, I., additional, Pearson, E.R., additional, Pedersen, O., additional, Pedersen, H.K.P., additional, Perry, M.H., additional, Pomares-Millan, H., additional, Ramisch, A., additional, Rasmussen, S.R., additional, Raverdi, V., additional, Ridderstrale, M., additional, Robertson, N., additional, Roderick, R.C., additional, Rodriquez, M., additional, Ruetten, H., additional, Rutters, F., additional, Sackett, W., additional, Scherer, N., additional, Schwenk, J.M., additional, Shah, N., additional, Sharma, S., additional, Sihinevich, I., additional, Sondertoft, N.B., additional, Staerfeldt, H., additional, Steckel-Hamann, B., additional, Teare, H., additional, Thomas, M.K., additional, Thomas, E.L., additional, Thomsen, H.S., additional, Thorand, B., additional, Thorne, C.E., additional, Tillner, J., additional, Troen Lundgaard, A.T.L., additional, Troll, M., additional, Tsirigos, K.D.T., additional, Tura, A., additional, Uhlen, M., additional, van Leeuwen, N., additional, van Oort, S., additional, Verkindt, H., additional, Vestergaard, H., additional, Viñuela, A., additional, Vogt, J.K, additional, Wad Sackett, P.W.S, additional, Wake, D., additional, Walker, M., additional, Wesolowska-Andersen, A., additional, Whitcher, B., additional, White, M.W., additional, and Wu, H., additional

Published
2023
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40. Elevated plasma Complement Factor H Regulating Protein 5 is associated with venous thromboembolism and COVID-19 severity

Author

Sanchez-Rivera, Laura, primary, Iglesias, Maria Jesus, additional, Ibrahim-Kosta, Manal, additional, Kral-Pointner, Julia Barbara, additional, Havervall, Sebastian, additional, Goumidi, Louisa, additional, Farm, Maria, additional, Munsch, Gaëlle, additional, Germain, Marine, additional, Smith, Philip, additional, Hong, Mun-Gwan, additional, Suchon, Pierre, additional, Naudin, Clément, additional, Boland, Anne, additional, Smadja, David M, additional, Holmström, Margareta, additional, Magnusson, Maria, additional, Silveira, Angela, additional, Uhlén, Mathias, additional, Renné, Thomas, additional, Martinez-Perez, Angel, additional, Emmerich, Joseph, additional, Deleuze, Jean-Francois, additional, Antovic, Jovan, additional, Assinger, Alice, additional, Fernandez, Jose Manuel Soria, additional, Thålin, Charlotte, additional, Schwenk, Jochen M, additional, Andres, Juan Carlos Souto, additional, Morange, Pierre-Emmanuel, additional, Butler, Lynn Marie, additional, Trégouët, David-Alexandre, additional, and Odeberg, Jacob, additional

Published
2022
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41. Identification of Endothelial Proteins in Plasma Associated With Cardiovascular Risk Factors

Author

Butler, Lynn, Iglesias, Maria J., Kruse, Larissa D., Sanchez-Rivera, Laura, Enge, Linnea, Dusart, Philip, Hong, Mun-Gwan, Uhlén, Mathias, Renné, Thomas, Schwenk, Jochen M., Bergstrom, Göran, and Odeberg, Jacob

Abstract

Objective: Endothelial cell (EC) dysfunction is a well-established response to cardiovascular disease risk factors, such as smoking and obesity. Risk factor exposure can modify EC signaling and behavior, leading to arterial and venous disease development. Here, we aimed to identify biomarker panels for the assessment of EC dysfunction, which could be useful for risk stratification or to monitor treatment response. Approach and Results: We used affinity proteomics to identify EC proteins circulating in plasma that were associated with cardiovascular disease risk factor exposure. Two hundred sixteen proteins, which we previously predicted to be EC-enriched across vascular beds, were measured in plasma samples (N=1005) from the population-based SCAPIS (Swedish Cardiopulmonary Bioimage Study) pilot. Thirty-eight of these proteins were associated with body mass index, total cholesterol, low-density lipoprotein, smoking, hypertension, or diabetes. Sex-specific analysis revealed that associations predominantly observed in female- or male-only samples were most frequently with the risk factors body mass index, or total cholesterol and smoking, respectively. We show a relationship between individual cardiovascular disease risk, calculated with the Framingham risk score, and the corresponding biomarker profiles. Conclusions: EC proteins in plasma could reflect vascular health status.

Published
2021

42. Identification of Endothelial Proteins in Plasma Associated With Cardiovascular Risk Factors

Author

Iglesias, Maria Jesus, Kruse, Larissa D., Sanchez-Rivera, Laura, Enge, Linnea, Dusart, Philip, Hong, Mun-Gwan, Uhlén, Mathias, Renne, Thomas, Schwenk, Jochen M., Bergstrom, Goran, Odeberg, Jacob, Butler, Lynn M., Iglesias, Maria Jesus, Kruse, Larissa D., Sanchez-Rivera, Laura, Enge, Linnea, Dusart, Philip, Hong, Mun-Gwan, Uhlén, Mathias, Renne, Thomas, Schwenk, Jochen M., Bergstrom, Goran, Odeberg, Jacob, and Butler, Lynn M.

Abstract

Objective: Endothelial cell (EC) dysfunction is a well-established response to cardiovascular disease risk factors, such as smoking and obesity. Risk factor exposure can modify EC signaling and behavior, leading to arterial and venous disease development. Here, we aimed to identify biomarker panels for the assessment of EC dysfunction, which could be useful for risk stratification or to monitor treatment response. Approach and Results: We used affinity proteomics to identify EC proteins circulating in plasma that were associated with cardiovascular disease risk factor exposure. Two hundred sixteen proteins, which we previously predicted to be EC-enriched across vascular beds, were measured in plasma samples (N=1005) from the population-based SCAPIS (Swedish Cardiopulmonary Bioimage Study) pilot. Thirty-eight of these proteins were associated with body mass index, total cholesterol, low-density lipoprotein, smoking, hypertension, or diabetes. Sex-specific analysis revealed that associations predominantly observed in female- or male-only samples were most frequently with the risk factors body mass index, or total cholesterol and smoking, respectively. We show a relationship between individual cardiovascular disease risk, calculated with the Framingham risk score, and the corresponding biomarker profiles. Conclusions: EC proteins in plasma could reflect vascular health status., QC 20211206

Published
2021
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45. 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

46. Profiles of histidine-rich glycoprotein associate with age and risk of all-cause mortality

Author

Hong, Mun-Gwan, Dodig-Crnkovic, Tea, Chen, Xu, Drobin, Kimi, Lee, Woojoo, Wang, Yunzhang, Edfors, Fredrik, Kotol, David, Thomas, Cecilia Engel, Sjöberg, Ronald, Odeberg, Jacob, Hamsten, Anders, Silveira, Angela, Hall, Per, Nilsson, Peter, Pawitan, Yudi, Uhlén, Mathias, Pedersen, Nancy L, Hägg, Sara, Magnusson, Patrik KE, Schwenk, Jochen M., Hong, Mun-Gwan, Dodig-Crnkovic, Tea, Chen, Xu, Drobin, Kimi, Lee, Woojoo, Wang, Yunzhang, Edfors, Fredrik, Kotol, David, Thomas, Cecilia Engel, Sjöberg, Ronald, Odeberg, Jacob, Hamsten, Anders, Silveira, Angela, Hall, Per, Nilsson, Peter, Pawitan, Yudi, Uhlén, Mathias, Pedersen, Nancy L, Hägg, Sara, Magnusson, Patrik KE, and Schwenk, Jochen M.

Abstract

Despite recognizing aging as a common risk factor of many human diseases, little is known about its molecular traits. To identify age-associated proteins circulating in human blood, we screened 156 individuals aged 50–92 using exploratory and multiplexed affinity proteomics assays. Profiling eight additional study sets (N = 3,987), performing antibody validation, and conducting a meta-analysis revealed a consistent age association (P = 6.61 × 10−6) for circulating histidine-rich glycoprotein (HRG). Sequence variants of HRG influenced how the protein was recognized in the immunoassays. Indeed, only the HRG profiles affected by rs9898 were associated with age and predicted the risk of mortality (HR = 1.25 per SD; 95% CI = 1.12–1.39; P = 6.45 × 10−5) during a follow-up period of 8.5 yr after blood sampling (IQR = 7.7–9.3 yr). Our affinity proteomics analysis found associations between the particular molecular traits of circulating HRG with age and all-cause mortality. The distinct profiles of this multipurpose protein could serve as an accessible and informative indicator of the physiological processes related to biological aging., QC 20200922

Published
2020
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47. Facets of individual-specific health signatures determined from longitudinal plasma proteome profiling

Author

Dodig-Crnkovic, Tea, Hong, Mun-Gwan, Thomas, Cecilia Engel, Häussler, Ragna S., Bendes, Annika, Dale, Matilda, Edfors, Fredrik, Forsström, Björn, Magnusson, Patrik K.E, Schuppe-Koistinen, Ina, Odeberg, Jacob, Fagerberg, Linn, Gummesson, Anders, Bergström, Göran, Uhlén, Mathias, Schwenk, Jochen M., Dodig-Crnkovic, Tea, Hong, Mun-Gwan, Thomas, Cecilia Engel, Häussler, Ragna S., Bendes, Annika, Dale, Matilda, Edfors, Fredrik, Forsström, Björn, Magnusson, Patrik K.E, Schuppe-Koistinen, Ina, Odeberg, Jacob, Fagerberg, Linn, Gummesson, Anders, Bergström, Göran, Uhlén, Mathias, and Schwenk, Jochen M.

Abstract

Background: Precision medicine approaches aim to tackle diseases on an individual level through molecular profiling. Despite the growing knowledge about diseases and the reported diversity of molecular phenotypes, the descriptions of human health on an individual level have been far less elaborate. Methods: To provide insights into the longitudinal protein signatures of well-being, we profiled blood plasma collected over one year from 101 clinically healthy individuals using multiplexed antibody assays. After applying an antibody validation scheme, we utilized > 700 protein profiles for in-depth analyses of the individuals’ short-term health trajectories. Findings: We found signatures of circulating proteomes to be highly individual-specific. Considering technical and longitudinal variability, we observed that 49% of the protein profiles were stable over one year. We also identified eight networks of proteins in which 11–242 proteins covaried over time. For each participant, there were unique protein profiles of which some could be explained by associations to genetic variants. Interpretation: This observational and non-interventional study identifyed noticeable diversity among clinically healthy subjects, and facets of individual-specific signatures emerged by monitoring the variability of the circulating proteomes over time. To enable more personal hence precise assessments of health states, longitudinal profiling of circulating proteomes can provide a valuable component for precision medicine approaches. Funding: This work was supported by the Erling Persson Foundation, the Swedish Heart and Lung Foundation, the Knut and Alice Wallenberg Foundation, Science for Life Laboratory, and the Swedish Research Council., QC 20200824

Published
2020
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48. 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, Dale, Matilda, Thomas, Cecilia Engel, Hong, Mun-Gwan, Häussler, Ragna S., Schwenk, Jochen M., Franks, Paul W., et al., Atabaki-Pasdar, Naeimeh, Dale, Matilda, Thomas, Cecilia Engel, Hong, Mun-Gwan, Häussler, Ragna S., Schwenk, Jochen M., Franks, Paul W., and et al.

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 (<5% or >= 5%) available for 1,514 participants. We applied LASSO (least absolute shrinkage and selection operator) to select features from the different layers of omics data and random forest analysis to develop the models. The prediction models included clinical and omics variables separately or in combination. A model including all omics and clinical variables yielded a cross-validated receiver operating characteristic area under the curve (ROCAUC) of 0.84 (95% CI 0.82, 0.86;p <0.001), which compared with a ROCAUC of 0.82 (95% CI 0.81, 0.83;p <0.001) for a model including 9 clinically accessible variables. The IMI DIRECT prediction models outperformed existing noninvasive NAFLD prediction tools. One limitation is that these analyses were performed in adults of European ancestry residing in northern Europe, and it is unknown how well these findings will translate to people of other ancestries and exposed to environmental risk factors that differ from those of the present cohort. Another key limitation of t, QC 20200720

Published
2020
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49. Molecular Profiling for Predictors of Radiosensitivity in Patients with Breast or Head-and-Neck Cancer

Author

Drobin, Kimi, Marczyk, Michal, Halle, Martin, Danielsson, Daniel, Papiez, Anna, Sangsuwan, Traimate, Bendes, Annika, Hong, Mun-Gwan, Qundos, Ulrika, Harms-Ringdahl, Mats, Wersall, Peter, Polanska, Joanna, Schwenk, Jochen M., Haghdoost, Siamak, Drobin, Kimi, Marczyk, Michal, Halle, Martin, Danielsson, Daniel, Papiez, Anna, Sangsuwan, Traimate, Bendes, Annika, Hong, Mun-Gwan, Qundos, Ulrika, Harms-Ringdahl, Mats, Wersall, Peter, Polanska, Joanna, Schwenk, Jochen M., and Haghdoost, Siamak

Abstract

Nearly half of all cancers are treated with radiotherapy alone or in combination with other treatments, where damage to normal tissues is a limiting factor for the treatment. Radiotherapy-induced adverse health effects, mostly of importance for cancer patients with long-term survival, may appear during or long time after finishing radiotherapy and depending on the patient's radiosensitivity. Currently, there is no assay available that can reliably predict the individual's response to radiotherapy. We profiled two study sets from breast (n = 29) and head-and-neck cancer patients (n = 74) that included radiosensitive patients and matched radioresistant controls. We studied 55 single nucleotide polymorphisms (SNPs) in 33 genes by DNA genotyping and 130 circulating proteins by affinity-based plasma proteomics. In both study sets, we discovered several plasma proteins with the predictive power to find radiosensitive patients (adjusted p < 0.05) and validated the two most predictive proteins (THPO and STIM1) by sandwich immunoassays. By integrating genotypic and proteomic data into an analysis model, it was found that the proteins CHIT1, PDGFB, PNKD, RP2, SERPINC1, SLC4A, STIM1, and THPO, as well as the VEGFA gene variant rs69947, predicted radiosensitivity of our breast cancer (AUC = 0.76) and head-and-neck cancer (AUC = 0.89) patients. In conclusion, circulating proteins and a SNP variant of VEGFA suggest that processes such as vascular growth capacity, immune response, DNA repair and oxidative stress/hypoxia may be involved in an individual's risk of experiencing radiation-induced toxicity., QC 20200622

Published
2020
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50. A reference map of potential determinants for the human serum metabolome

Author

Bar, N., Korem, T., Weissbrod, O., Zeevi, D., Rothschild, D., Leviatan, S., Kosower, N., Lotan-Pompan, M., Weinberger, A., Le Roy, C. I., Menni, C., Visconti, A., Falchi, M., Spector, T. D., Vestergaard, H., Arumugam, M., Hansen, T., Allin, K., Hong, Mun-Gwan, Schwenk, Jochen M., Häussler, Ragna S., Dale, Matilda, Giorgino, T., Rodriquez, M., Perry, M., Nice, R., McDonald, T., Hattersley, A., Jones, A., Graefe-Mody, U., Baum, P., Grempler, R., Thomas, C. E., Masi, F. D., Brorsson, C. A., Mazzoni, G., Allesøe, R., Rasmussen, S., Gudmundsdóttir, V., Nielsen, A. M., Banasik, K., Tsirigos, K., Nilsson, B., Pedersen, H., Brunak, S., Karaderi, T., Lundgaard, A. T., Johansen, J., Gupta, R., Sackett, P. W., Tillner, J., Lehr, T., Scherer, N., Dings, C., Sihinevich, I., Loftus, H., Cabrelli, L., McEvoy, D., Mari, A., Bizzotto, R., Tura, A., ’t Hart, L., Dekkers, K., Leeuwen, N., Slieker, R., Rutters, F., Beulens, J., Nijpels, G., Koopman, A., Oort, S., Groeneveld, L., Groop, L., Elders, P., Viñuela, A., Ramisch, A., Dermitzakis, E., Ehrhardt, B., Jennison, C., Froguel, P., Canouil, M., Boneford, A., McVittie, I., Wake, D., Frau, F., Staerfeldt, H. -H, Adragni, K., Thomas, M., Wu, H., Pavo, I., Steckel-Hamann, B., Thomsen, H., Giordano, G. N., Fitipaldi, H., Ridderstråle, M., Kurbasic, A., Pasdar, N. A., Pomares-Millan, H., Mutie, P., Koivula, R., McRobert, N., McCarthy, M., Wesolowska-Andersen, A., Mahajan, A., Abdalla, M., Fernandez, J., Holl, R., Heggie, A., Deshmukh, H., Hennige, A., Bianzano, S., Thorand, B., Sharma, S., Grallert, H., Adam, J., Troll, M., Fritsche, A., Hill, A., Thorne, C., Hudson, M., Kuulasmaa, T., Vangipurapu, J., Laakso, M., Cederberg, H., Kokkola, T., Jiao, Y., Gough, S., Robertson, N., Verkindt, H., Raverdi, V., Caiazzo, R., Pattou, F., White, M., Donnelly, L., Brown, A., Palmer, C., Davtian, D., Dawed, A., Forgie, I., Pearson, E., Ruetten, H., Musholt, P., Bell, J., Thomas, E. L., Whitcher, B., Haid, M., Nicolay, C., Mourby, M., Kaye, J., Shah, N., Teare, H., Frost, G., Jablonka, B., Uhlen, M., Eriksen, R., Vogt, J., Dutta, A., Jonsson, A., Engelbrechtsen, L., Forman, A., Sondertoft, N., de Preville, N., Baltauss, T., Walker, M., Gassenhuber, J., Klintenberg, M., Bergstrom, M., Ferrer, J., Adamski, J., Franks, P. W., Pedersen, O., Segal, E., consortium, The IMI DIRECT, Bar, N., Korem, T., Weissbrod, O., Zeevi, D., Rothschild, D., Leviatan, S., Kosower, N., Lotan-Pompan, M., Weinberger, A., Le Roy, C. I., Menni, C., Visconti, A., Falchi, M., Spector, T. D., Vestergaard, H., Arumugam, M., Hansen, T., Allin, K., Hong, Mun-Gwan, Schwenk, Jochen M., Häussler, Ragna S., Dale, Matilda, Giorgino, T., Rodriquez, M., Perry, M., Nice, R., McDonald, T., Hattersley, A., Jones, A., Graefe-Mody, U., Baum, P., Grempler, R., Thomas, C. E., Masi, F. D., Brorsson, C. A., Mazzoni, G., Allesøe, R., Rasmussen, S., Gudmundsdóttir, V., Nielsen, A. M., Banasik, K., Tsirigos, K., Nilsson, B., Pedersen, H., Brunak, S., Karaderi, T., Lundgaard, A. T., Johansen, J., Gupta, R., Sackett, P. W., Tillner, J., Lehr, T., Scherer, N., Dings, C., Sihinevich, I., Loftus, H., Cabrelli, L., McEvoy, D., Mari, A., Bizzotto, R., Tura, A., ’t Hart, L., Dekkers, K., Leeuwen, N., Slieker, R., Rutters, F., Beulens, J., Nijpels, G., Koopman, A., Oort, S., Groeneveld, L., Groop, L., Elders, P., Viñuela, A., Ramisch, A., Dermitzakis, E., Ehrhardt, B., Jennison, C., Froguel, P., Canouil, M., Boneford, A., McVittie, I., Wake, D., Frau, F., Staerfeldt, H. -H, Adragni, K., Thomas, M., Wu, H., Pavo, I., Steckel-Hamann, B., Thomsen, H., Giordano, G. N., Fitipaldi, H., Ridderstråle, M., Kurbasic, A., Pasdar, N. A., Pomares-Millan, H., Mutie, P., Koivula, R., McRobert, N., McCarthy, M., Wesolowska-Andersen, A., Mahajan, A., Abdalla, M., Fernandez, J., Holl, R., Heggie, A., Deshmukh, H., Hennige, A., Bianzano, S., Thorand, B., Sharma, S., Grallert, H., Adam, J., Troll, M., Fritsche, A., Hill, A., Thorne, C., Hudson, M., Kuulasmaa, T., Vangipurapu, J., Laakso, M., Cederberg, H., Kokkola, T., Jiao, Y., Gough, S., Robertson, N., Verkindt, H., Raverdi, V., Caiazzo, R., Pattou, F., White, M., Donnelly, L., Brown, A., Palmer, C., Davtian, D., Dawed, A., Forgie, I., Pearson, E., Ruetten, H., Musholt, P., Bell, J., Thomas, E. L., Whitcher, B., Haid, M., Nicolay, C., Mourby, M., Kaye, J., Shah, N., Teare, H., Frost, G., Jablonka, B., Uhlen, M., Eriksen, R., Vogt, J., Dutta, A., Jonsson, A., Engelbrechtsen, L., Forman, A., Sondertoft, N., de Preville, N., Baltauss, T., Walker, M., Gassenhuber, J., Klintenberg, M., Bergstrom, M., Ferrer, J., Adamski, J., Franks, P. W., Pedersen, O., Segal, E., and consortium, The IMI DIRECT

Abstract

The serum metabolome contains a plethora of biomarkers and causative agents of various diseases, some of which are endogenously produced and some that have been taken up from the environment1. The origins of specific compounds are known, including metabolites that are highly heritable2,3, or those that are influenced by the gut microbiome4, by lifestyle choices such as smoking5, or by diet6. However, the key determinants of most metabolites are still poorly understood. Here we measured the levels of 1,251 metabolites in serum samples from a unique and deeply phenotyped healthy human cohort of 491 individuals. We applied machine-learning algorithms to predict metabolite levels in held-out individuals on the basis of host genetics, gut microbiome, clinical parameters, diet, lifestyle and anthropometric measurements, and obtained statistically significant predictions for more than 76% of the profiled metabolites. Diet and microbiome had the strongest predictive power, and each explained hundreds of metabolites—in some cases, explaining more than 50% of the observed variance. We further validated microbiome-related predictions by showing a high replication rate in two geographically independent cohorts7,8 that were not available to us when we trained the algorithms. We used feature attribution analysis9 to reveal specific dietary and bacterial interactions. We further demonstrate that some of these interactions might be causal, as some metabolites that we predicted to be positively associated with bread were found to increase after a randomized clinical trial of bread intervention. Overall, our results reveal potential determinants of more than 800 metabolites, paving the way towards a mechanistic understanding of alterations in metabolites under different conditions and to designing interventions for manipulating the levels of circulating metabolites., QC 20211001

Published
2020
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