Search

Your search keyword '"Hong, Mun-Gwan"' showing total 5 results
Start Over Author "Hong, Mun-Gwan" Language undetermined
5 results on '"Hong, Mun-Gwan"'

1. 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, Schottmaier, 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, Odeberg, Jacob, Naudin, Clément [0000-0002-6777-9587], Munsch, Gaëlle [0000-0003-1564-9825], Goumidi, Louisa [0000-0003-4164-8471], Hong, Mun-Gwan [0000-0001-8603-8293], Schottmaier, Waltraud [0000-0003-0550-4120], Dusart, Philip [0000-0003-2747-3214], Kotol, David [0000-0002-5388-3826], Edfors, Fredrik [0000-0002-0017-7987], Koprulu, Mine [0000-0001-6870-4539], Pietzner, Maik [0000-0003-3437-9963], Langenberg, Claudia [0000-0002-5017-7344], Damrauer, Scott M [0000-0001-8009-1632], Johnson, Andrew D [0000-0001-6369-5178], Klarin, Derek M [0000-0002-4636-5780], Silveira, Angela [0000-0003-2063-4935], Uhlén, Mathias [0000-0002-4858-8056], Martinez-Perez, Angel [0000-0002-5934-1454], Deleuze, Jean-Francois [0000-0002-5358-4463], Assinger, Alice [0000-0002-5670-5910], Schwenk, Jochen M [0000-0001-8141-8449], Souto Andres, Joan Carles [0000-0003-2092-5142], Morange, Pierre-Emmanuel [0000-0002-9065-722X], Butler, Lynn Marie [0000-0002-2352-8217], Trégouët, David-Alexandre [0000-0001-9084-7800], Odeberg, Jacob [0000-0003-0996-1644], and Apollo - University of Cambridge Repository

Subjects
Complement Factor H, Humans, Factor V, Complement System Proteins, Venous Thromboembolism, Complement Activation, Biomarkers
Abstract

Venous thromboembolism (VTE) is a common, multi-causal disease with potentially serious short- and long-term complications. In clinical practice, there is a need for improved plasma biomarker-based tools for VTE diagnosis and risk prediction. Here we show, using proteomics profiling to screen plasma from patients with suspected acute VTE, and several case-control studies for VTE, how Complement Factor H Related 5 protein (CFHR5), a regulator of the alternative pathway of complement activation, is a VTE-associated plasma biomarker. In plasma, higher CFHR5 levels are associated with increased thrombin generation potential and recombinant CFHR5 enhanced platelet activation in vitro. GWAS analysis of ~52,000 participants identifies six loci associated with CFHR5 plasma levels, but Mendelian randomization do not demonstrate causality between CFHR5 and VTE. Our results indicate an important role for the regulation of the alternative pathway of complement activation in VTE and that CFHR5 represents a potential diagnostic and/or risk predictive plasma biomarker.

Published
2023

2. 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, Brunak, Søren, IMI DIRECT Consortium, Lundgaard, Agnete Troen [0000-0001-7447-6560], Hernández Medina, Ricardo [0000-0001-6373-2362], Johansen, Joachim [0000-0001-7052-1870], Niu, Lili [0000-0003-4571-4368], Biel, Jorge Hernansanz [0000-0002-3125-2951], Leal Rodríguez, Cristina [0000-0002-3133-0630], Benros, Michael Eriksen [0000-0003-4939-9465], Pedersen, Anders Gorm [0000-0001-9650-8965], Jacobsen, Ulrik Plesner [0000-0001-9181-6854], Koivula, Robert [0000-0002-1646-4163], Vinuela, Ana [0000-0003-3771-8537], Haid, Mark [0000-0001-6118-1333], Hong, Mun-Gwan [0000-0001-8603-8293], Kennedy, Gwen [0000-0002-9856-3236], Thomas, E Louise [0000-0003-4235-4694], Frost, Gary [0000-0003-0529-6325], Hansen, Tue Haldor [0000-0001-5948-8993], Kaye, Jane [0000-0002-7311-4725], Hattersley, Andrew [0000-0001-5620-473X], Ridderstråle, Martin [0000-0002-3270-9167], Pedersen, Oluf [0000-0002-3321-3972], Hansen, Torben [0000-0001-8748-3831], Schwenk, Jochen M [0000-0001-8141-8449], Rasmussen, Simon [0000-0001-6323-9041], Brunak, Søren [0000-0003-0316-5866], and Apollo - University of Cambridge Repository

Subjects
Deep Learning, Diabetes Mellitus, Type 2, Humans, Algorithms
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

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

Author

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

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

Funder: Henning och Johan Throne-Holsts, Funder: Hans Werthén, Funder: Swedish Foundation for Strategic Research, Funder: NIHR clinical senior lecturer fellowship, Funder: Wellcome Trust Senior Investigator, Funder: NIHR Exeter Clinical Research Facility, Funder: Science for Life Laboratory (Plasma Profiling Facility), Funder: Knut and Alice Wallenberg Foundation (Human Protein Atlas), Funder: Erling-Persson Foundation (KTH Centre for Precision Medicine), Background: Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and, ultimately, hepatocellular carcinomas. We sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning. Methods and findings: We utilized the baseline data from IMI DIRECT, a multicenter prospective cohort study of 3,029 European-ancestry adults recently diagnosed with T2D (n = 795) or at high risk of developing the disease (n = 2,234). Multi-omics (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI-image-derived liver fat content (

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

Author

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

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

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and, ultimately, hepatocellular carcinomas. We sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning. METHODS AND FINDINGS: We utilized the baseline data from IMI DIRECT, a multicenter prospective cohort study of 3,029 European-ancestry adults recently diagnosed with T2D (n = 795) or at high risk of developing the disease (n = 2,234). Multi-omics (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI-image-derived liver fat content (

5. Machine-Learning Algorithm Predicts Early Type 2 Diabetes Remission following Roux-en-Y Gastric Bypass

Author

Thomas, Cecilia E., Raverdi, Violeta, Pedersen, Helle Krogh, Gassenhuber, Johann, Brorsson, Caroline, Gudmundsdottir, Valborg, Vinuela, Ana, Howald, Cedric, Wu, Han, Karina Banasik, Yengo, Loic, Haid, Mark, Thomas, Melissa K., Hinterholzer, Michaela, Canouil, Mickael, Hong, Mun Gwan, Davidsen, Peter, Gupta, Ramneek, Sharma, Sapna, Wahl, Simone, Dermitzakis, Emmanouil T., Grallert, Harald, Schwenk, Jochen M., Farmen, Mark, Brunak, Soren, and Pattou, Francois

Catalog

Books, media, physical & digital resources
See catalog results