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18 results on '"Raverdy, Violeta"'

1. A Federated Database for Obesity Research: An IMI-SOPHIA Study

Author

RWE/Causal inference, Child Health, Delfin, Carl, Dragan, Iulian, Kuznetsov, Dmitry, Tajes, Juan Fernandez, Smit, Femke, Coral, Daniel E, Farzaneh, Ali, Haugg, André, Hungele, Andreas, Niknejad, Anne, Hall, Christopher, Jacobs, Daan, Marek, Diana, Fraser, Diane P, Thuillier, Dorothee, Ahmadizar, Fariba, Mehl, Florence, Pattou, Francois, Burdet, Frederic, Hawkes, Gareth, Arts, Ilja C W, Blanch, Jordi, Van Soest, Johan, Fernández-Real, José-Manuel, Boehl, Juergen, Fink, Katharina, van Greevenbroek, Marleen M J, Kavousi, Maryam, Minten, Michiel, Prinz, Nicole, Ipsen, Niels, Franks, Paul W, Ramos, Rafael, Holl, Reinhard W, Horban, Scott, Duarte-Salles, Talita, Tran, Van Du T, Raverdy, Violeta, Leal, Yenny, Lenart, Adam, Pearson, Ewan, Sparsø, Thomas, Giordano, Giuseppe N, Ioannidis, Vassilios, Soh, Keng, Frayling, Timothy M, Le Roux, Carel W, Ibberson, Mark, RWE/Causal inference, Child Health, Delfin, Carl, Dragan, Iulian, Kuznetsov, Dmitry, Tajes, Juan Fernandez, Smit, Femke, Coral, Daniel E, Farzaneh, Ali, Haugg, André, Hungele, Andreas, Niknejad, Anne, Hall, Christopher, Jacobs, Daan, Marek, Diana, Fraser, Diane P, Thuillier, Dorothee, Ahmadizar, Fariba, Mehl, Florence, Pattou, Francois, Burdet, Frederic, Hawkes, Gareth, Arts, Ilja C W, Blanch, Jordi, Van Soest, Johan, Fernández-Real, José-Manuel, Boehl, Juergen, Fink, Katharina, van Greevenbroek, Marleen M J, Kavousi, Maryam, Minten, Michiel, Prinz, Nicole, Ipsen, Niels, Franks, Paul W, Ramos, Rafael, Holl, Reinhard W, Horban, Scott, Duarte-Salles, Talita, Tran, Van Du T, Raverdy, Violeta, Leal, Yenny, Lenart, Adam, Pearson, Ewan, Sparsø, Thomas, Giordano, Giuseppe N, Ioannidis, Vassilios, Soh, Keng, Frayling, Timothy M, Le Roux, Carel W, and Ibberson, Mark

Published
2024

2. A Federated Database for Obesity Research:An IMI-SOPHIA Study

Author

Delfin, Carl, Dragan, Iulian, Kuznetsov, Dmitry, Tajes, Juan Fernandez, Smit, Femke, Coral, Daniel E., Farzaneh, Ali, Haugg, Andre, Hungele, Andreas, Niknejad, Anne, Hall, Christopher, Jacobs, Daan, Marek, Diana, Fraser, Diane P., Thuillier, Dorothee, Ahmadizar, Fariba, Mehl, Florence, Pattou, Francois, Burdet, Frederic, Hawkes, Gareth, Arts, Ilja C. W., Blanch, Jordi, Van Soest, Johan, Fernandez-Real, Jose-Manuel, Boehl, Juergen, Fink, Katharina, van Greevenbroek, Marleen M. J., Kavousi, Maryam, Minten, Michiel, Prinz, Nicole, Ipsen, Niels, Franks, Paul W., Ramos, Rafael, Holl, Reinhard W., Horban, Scott, Duarte-Salles, Talita, Tran, Van Du T., Raverdy, Violeta, Leal, Yenny, Lenart, Adam, Pearson, Ewan, Sparso, Thomas, Giordano, Giuseppe N., Ioannidis, Vassilios, Soh, Keng, Frayling, Timothy M., Le Roux, Carel W., Ibberson, Mark, Delfin, Carl, Dragan, Iulian, Kuznetsov, Dmitry, Tajes, Juan Fernandez, Smit, Femke, Coral, Daniel E., Farzaneh, Ali, Haugg, Andre, Hungele, Andreas, Niknejad, Anne, Hall, Christopher, Jacobs, Daan, Marek, Diana, Fraser, Diane P., Thuillier, Dorothee, Ahmadizar, Fariba, Mehl, Florence, Pattou, Francois, Burdet, Frederic, Hawkes, Gareth, Arts, Ilja C. W., Blanch, Jordi, Van Soest, Johan, Fernandez-Real, Jose-Manuel, Boehl, Juergen, Fink, Katharina, van Greevenbroek, Marleen M. J., Kavousi, Maryam, Minten, Michiel, Prinz, Nicole, Ipsen, Niels, Franks, Paul W., Ramos, Rafael, Holl, Reinhard W., Horban, Scott, Duarte-Salles, Talita, Tran, Van Du T., Raverdy, Violeta, Leal, Yenny, Lenart, Adam, Pearson, Ewan, Sparso, Thomas, Giordano, Giuseppe N., Ioannidis, Vassilios, Soh, Keng, Frayling, Timothy M., Le Roux, Carel W., and Ibberson, Mark

Abstract

Obesity is considered by many as a lifestyle choice rather than a chronic progressive disease. The Innovative Medicines Initiative (IMI) SOPHIA (Stratification of Obesity Phenotypes to Optimize Future Obesity Therapy) project is part of a momentum shift aiming to provide better tools for the stratification of people with obesity according to disease risk and treatment response. One of the challenges to achieving these goals is that many clinical cohorts are siloed, limiting the potential of combined data for biomarker discovery. In SOPHIA, we have addressed this challenge by setting up a federated database building on open-source DataSHIELD technology. The database currently federates 16 cohorts that are accessible via a central gateway. The database is multi-modal, including research studies, clinical trials, and routine health data, and is accessed using the R statistical programming environment where statistical and machine learning analyses can be performed at a distance without any disclosure of patient-level data. We demonstrate the use of the database by providing a proof-of-concept analysis, performing a federated linear model of BMI and systolic blood pressure, pooling all data from 16 studies virtually without any analyst seeing individual patient-level data. This analysis provided similar point estimates compared to a meta-analysis of the 16 individual studies. Our approach provides a benchmark for reproducible, safe federated analyses across multiple study types provided by multiple stakeholders.

Published
2024

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

Author

Allesøe, Rosa Lundbye, Lundgaard, Agnete Troen, Hernández Medina, Ricardo, Aguayo-Orozco, Alejandro, Johansen, Joachim, Nissen, Jakob Nybo, Brorsson, Caroline, Mazzoni, Gianluca, Niu, Lili, Biel, Jorge Hernansanz, Brasas, Valentas, Webel, Henry, Benros, Michael Eriksen, Pedersen, Anders Gorm, Chmura, Piotr Jaroslaw, Jacobsen, Ulrik Plesner, Mari, Andrea, Koivula, Robert, Mahajan, Anubha, Vinuela, Ana, Tajes, Juan Fernandez, Sharma, Sapna, Haid, Mark, Hong, Mun-Gwan, Musholt, Petra B., De Masi, Federico, Vogt, Josef, Pedersen, Helle Krogh, Gudmundsdottir, Valborg, Jones, Angus, Kennedy, Gwen, Bell, Jimmy, Thomas, E. Louise, Frost, Gary, Thomsen, Henrik, Hansen, Elizaveta, Hansen, Tue Haldor, Vestergaard, Henrik, Muilwijk, Mirthe, Blom, Marieke T., ‘t Hart, Leen M., Pattou, Francois, Raverdy, Violeta, Brage, Soren, Kokkola, Tarja, Heggie, Alison, McEvoy, Donna, Mourby, Miranda, Kaye, Jane, Hattersley, Andrew, McDonald, Timothy, Ridderstråle, Martin, Walker, Mark, Forgie, Ian, Giordano, Giuseppe N., Pavo, Imre, Ruetten, Hartmut, Pedersen, Oluf, Hansen, Torben, Dermitzakis, Emmanouil, Franks, Paul W., Schwenk, Jochen M., Adamski, Jerzy, McCarthy, Mark I., Pearson, Ewan, Banasik, Karina, Rasmussen, Simon, Brunak, Søren, Froguel, Philippe, Thomas, Cecilia Engel, Häussler, Ragna S., Beulens, Joline, Rutters, Femke, Nijpels, Giel, van Oort, Sabine, Groeneveld, Lenka, Elders, Petra, Giorgino, Toni, Rodriquez, Marianne, Nice, Rachel, Perry, Mandy, Bianzano, Susanna, Graefe-Mody, Ulrike, Hennige, Anita, Grempler, Rolf, Baum, Patrick, Stærfeldt, Hans Henrik, Shah, Nisha, Teare, Harriet, Ehrhardt, Beate, Tillner, Joachim, Dings, Christiane, Lehr, Thorsten, Scherer, Nina, Sihinevich, Iryna, Cabrelli, Louise, Loftus, Heather, Bizzotto, Roberto, Tura, Andrea, Dekkers, Koen, Allesøe, Rosa Lundbye, Lundgaard, Agnete Troen, Hernández Medina, Ricardo, Aguayo-Orozco, Alejandro, Johansen, Joachim, Nissen, Jakob Nybo, Brorsson, Caroline, Mazzoni, Gianluca, Niu, Lili, Biel, Jorge Hernansanz, Brasas, Valentas, Webel, Henry, Benros, Michael Eriksen, Pedersen, Anders Gorm, Chmura, Piotr Jaroslaw, Jacobsen, Ulrik Plesner, Mari, Andrea, Koivula, Robert, Mahajan, Anubha, Vinuela, Ana, Tajes, Juan Fernandez, Sharma, Sapna, Haid, Mark, Hong, Mun-Gwan, Musholt, Petra B., De Masi, Federico, Vogt, Josef, Pedersen, Helle Krogh, Gudmundsdottir, Valborg, Jones, Angus, Kennedy, Gwen, Bell, Jimmy, Thomas, E. Louise, Frost, Gary, Thomsen, Henrik, Hansen, Elizaveta, Hansen, Tue Haldor, Vestergaard, Henrik, Muilwijk, Mirthe, Blom, Marieke T., ‘t Hart, Leen M., Pattou, Francois, Raverdy, Violeta, Brage, Soren, Kokkola, Tarja, Heggie, Alison, McEvoy, Donna, Mourby, Miranda, Kaye, Jane, Hattersley, Andrew, McDonald, Timothy, Ridderstråle, Martin, Walker, Mark, Forgie, Ian, Giordano, Giuseppe N., Pavo, Imre, Ruetten, Hartmut, Pedersen, Oluf, Hansen, Torben, Dermitzakis, Emmanouil, Franks, Paul W., Schwenk, Jochen M., Adamski, Jerzy, McCarthy, Mark I., Pearson, Ewan, Banasik, Karina, Rasmussen, Simon, Brunak, Søren, Froguel, Philippe, Thomas, Cecilia Engel, Häussler, Ragna S., Beulens, Joline, Rutters, Femke, Nijpels, Giel, van Oort, Sabine, Groeneveld, Lenka, Elders, Petra, Giorgino, Toni, Rodriquez, Marianne, Nice, Rachel, Perry, Mandy, Bianzano, Susanna, Graefe-Mody, Ulrike, Hennige, Anita, Grempler, Rolf, Baum, Patrick, Stærfeldt, Hans Henrik, Shah, Nisha, Teare, Harriet, Ehrhardt, Beate, Tillner, Joachim, Dings, Christiane, Lehr, Thorsten, Scherer, Nina, Sihinevich, Iryna, Cabrelli, Louise, Loftus, Heather, Bizzotto, Roberto, Tura, Andrea, and Dekkers, Koen

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., QC 20230626

Published
2023
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4. 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., Viñuela, Ana, 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

Abstract

We evaluate the shared genetic regulation of mRNA molecules, proteins and metabolites derived from whole blood from 3029 human donors. We find abundant allelic heterogeneity, where multiple variants regulate a particular molecular phenotype, and pleiotropy, where a single variant associates with multiple molecular phenotypes over multiple genomic regions. The highest proportion of share genetic regulation is detected between gene expression and proteins (66.6%), with a further median shared genetic associations across 49 different tissues of 78.3% and 62.4% between plasma proteins and gene expression. We represent the genetic and molecular associations in networks including 2828 known GWAS variants, showing that GWAS variants are more often connected to gene expression in trans than other molecular phenotypes in the network. Our work provides a roadmap to understanding molecular networks and deriving the underlying mechanism of action of GWAS variants using different molecular phenotypes in an accessible tissue.

Published
2023

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, Adragni, Kofi, Allesøe, Rosa Lundbye L., Artati, Anna A., Arumugam, Manimozhiyan, Atabaki-Pasdar, Naeimeh, Baltauss, Tania, Banasik, Karina, Barnett, Anna L., Baum, Patrick, Bell, Jimmy D., Beulens, Joline W., Bianzano, Susanna B., Bizzotto, Roberto, Bonnefond, Amelie, Cabrelli, Louise, Dale, Matilda, Dawed, Adem Y., de Preville, Nathalie, Dekkers, Koen F., Deshmukh, Harshal A., Dings, Christiane, Donnelly, Louise, Dutta, Avirup, Ehrhardt, Beate, Engelbrechtsen, Line, Eriksen, Rebeca, Fan, Yong, Ferrer, Jorge, Fitipaldi, Hugo, Forman, Annemette, Fritsche, Andreas, Froguel, Philippe, Gassenhuber, Johann, Gough, Stephen, Graefe-Mody, Ulrike, Grempler, Rolf, Groeneveld, Lenka, Groop, Leif, Gudmundsdóttir, Valborg, Gupta, Ramneek, Hennige, Anita M.H., Hill, Anita V., Holl, Reinhard W., Hudson, Michelle, Jacobsen, Ulrik Plesner, Jennison, Christopher, Johansen, Joachim, Jonsson, Anna, Karaderi, Tugce, Kaye, Jane, Kennedy, Gwen, Klintenberg, Maria, Kuulasmaa, Teemu, Lehr, Thorsten, Loftus, Heather, Lundgaard, Agnete Troen T., Mazzoni, Gianluca, McRobert, Nicky, McVittie, Ian, Nice, Rachel, Nicolay, Claudia, Nijpels, Giel, Palmer, Colin N., Pedersen, Helle K., Perry, Mandy H., Pomares-Millan, Hugo, Prehn, Cornelia P., Ramisch, Anna, Rasmussen, Simon, Robertson, Neil, Rodriquez, Marianne, Sackett, Peter, Scherer, Nina, Shah, Nisha, Sihinevich, Iryna, Slieker, Roderick C., Sondertoft, Nadja B., Steckel-Hamann, Birgit, Thomas, Melissa K., Thomas, Cecilia Engel E., Thomas, Elizabeth Louise L., Thorand, Barbara, Thorne, Claire E., Tillner, Joachim, Tura, Andrea, Uhlen, Mathias, van Leeuwen, Nienke, van Oort, Sabine, Verkindt, Helene, Vogt, Josef, Wad Sackett, Peter W., Wesolowska-Andersen, Agata, Whitcher, Brandon, White, Margaret W., Adamski, Jerzy, Schwenk, Jochen M., Pearson, Ewan R., Dermitzakis, Emmanouil T., Viñuela, Ana, 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, Adragni, Kofi, Allesøe, Rosa Lundbye L., Artati, Anna A., Arumugam, Manimozhiyan, Atabaki-Pasdar, Naeimeh, Baltauss, Tania, Banasik, Karina, Barnett, Anna L., Baum, Patrick, Bell, Jimmy D., Beulens, Joline W., Bianzano, Susanna B., Bizzotto, Roberto, Bonnefond, Amelie, Cabrelli, Louise, Dale, Matilda, Dawed, Adem Y., de Preville, Nathalie, Dekkers, Koen F., Deshmukh, Harshal A., Dings, Christiane, Donnelly, Louise, Dutta, Avirup, Ehrhardt, Beate, Engelbrechtsen, Line, Eriksen, Rebeca, Fan, Yong, Ferrer, Jorge, Fitipaldi, Hugo, Forman, Annemette, Fritsche, Andreas, Froguel, Philippe, Gassenhuber, Johann, Gough, Stephen, Graefe-Mody, Ulrike, Grempler, Rolf, Groeneveld, Lenka, Groop, Leif, Gudmundsdóttir, Valborg, Gupta, Ramneek, Hennige, Anita M.H., Hill, Anita V., Holl, Reinhard W., Hudson, Michelle, Jacobsen, Ulrik Plesner, Jennison, Christopher, Johansen, Joachim, Jonsson, Anna, Karaderi, Tugce, Kaye, Jane, Kennedy, Gwen, Klintenberg, Maria, Kuulasmaa, Teemu, Lehr, Thorsten, Loftus, Heather, Lundgaard, Agnete Troen T., Mazzoni, Gianluca, McRobert, Nicky, McVittie, Ian, Nice, Rachel, Nicolay, Claudia, Nijpels, Giel, Palmer, Colin N., Pedersen, Helle K., Perry, Mandy H., Pomares-Millan, Hugo, Prehn, Cornelia P., Ramisch, Anna, Rasmussen, Simon, Robertson, Neil, Rodriquez, Marianne, Sackett, Peter, Scherer, Nina, Shah, Nisha, Sihinevich, Iryna, Slieker, Roderick C., Sondertoft, Nadja B., Steckel-Hamann, Birgit, Thomas, Melissa K., Thomas, Cecilia Engel E., Thomas, Elizabeth Louise L., Thorand, Barbara, Thorne, Claire E., Tillner, Joachim, Tura, Andrea, Uhlen, Mathias, van Leeuwen, Nienke, van Oort, Sabine, Verkindt, Helene, Vogt, Josef, Wad Sackett, Peter W., Wesolowska-Andersen, Agata, Whitcher, Brandon, White, Margaret W., Adamski, Jerzy, Schwenk, Jochen M., Pearson, Ewan R., Dermitzakis, Emmanouil T., and Viñuela, Ana

Abstract

We evaluate the shared genetic regulation of mRNA molecules, proteins and metabolites derived from whole blood from 3029 human donors. We find abundant allelic heterogeneity, where multiple variants regulate a particular molecular phenotype, and pleiotropy, where a single variant associates with multiple molecular phenotypes over multiple genomic regions. The highest proportion of share genetic regulation is detected between gene expression and proteins (66.6%), with a further median shared genetic associations across 49 different tissues of 78.3% and 62.4% between plasma proteins and gene expression. We represent the genetic and molecular associations in networks including 2828 known GWAS variants, showing that GWAS variants are more often connected to gene expression in trans than other molecular phenotypes in the network. Our work provides a roadmap to understanding molecular networks and deriving the underlying mechanism of action of GWAS variants using different molecular phenotypes in an accessible tissue.

Published
2023

6. 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

7. 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

8. 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, Adragni, Kofi, Allesøe, Rosa Lundbye L., Artati, Anna A., Arumugam, Manimozhiyan, Atabaki-Pasdar, Naeimeh, Baltauss, Tania, Banasik, Karina, Barnett, Anna L., Baum, Patrick, Bell, Jimmy D., Beulens, Joline W., Bianzano, Susanna B., Bizzotto, Roberto, Bonnefond, Amelie, Cabrelli, Louise, Dale, Matilda, Dawed, Adem Y., de Preville, Nathalie, Dekkers, Koen F., Deshmukh, Harshal A., Dings, Christiane, Donnelly, Louise, Dutta, Avirup, Ehrhardt, Beate, Engelbrechtsen, Line, Eriksen, Rebeca, Fan, Yong, Ferrer, Jorge, Fitipaldi, Hugo, Forman, Annemette, Fritsche, Andreas, Froguel, Philippe, Gassenhuber, Johann, Gough, Stephen, Graefe-Mody, Ulrike, Grempler, Rolf, Groeneveld, Lenka, Groop, Leif, Gudmundsdóttir, Valborg, Gupta, Ramneek, Hennige, Anita M.H., Hill, Anita V., Holl, Reinhard W., Hudson, Michelle, Jacobsen, Ulrik Plesner, Jennison, Christopher, Johansen, Joachim, Jonsson, Anna, Karaderi, Tugce, Kaye, Jane, Kennedy, Gwen, Klintenberg, Maria, Kuulasmaa, Teemu, Lehr, Thorsten, Loftus, Heather, Lundgaard, Agnete Troen T., Mazzoni, Gianluca, McRobert, Nicky, McVittie, Ian, Nice, Rachel, Nicolay, Claudia, Nijpels, Giel, Palmer, Colin N., Pedersen, Helle K., Perry, Mandy H., Pomares-Millan, Hugo, Prehn, Cornelia P., Ramisch, Anna, Rasmussen, Simon, Robertson, Neil, Rodriquez, Marianne, Sackett, Peter, Scherer, Nina, Shah, Nisha, Sihinevich, Iryna, Slieker, Roderick C., Sondertoft, Nadja B., Steckel-Hamann, Birgit, Thomas, Melissa K., Thomas, Cecilia Engel E., Thomas, Elizabeth Louise L., Thorand, Barbara, Thorne, Claire E., Tillner, Joachim, Tura, Andrea, Uhlen, Mathias, van Leeuwen, Nienke, van Oort, Sabine, Verkindt, Helene, Vogt, Josef, Wad Sackett, Peter W., Wesolowska-Andersen, Agata, Whitcher, Brandon, White, Margaret W., Adamski, Jerzy, Schwenk, Jochen M., Pearson, Ewan R., Dermitzakis, Emmanouil T., Viñuela, Ana, 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, Adragni, Kofi, Allesøe, Rosa Lundbye L., Artati, Anna A., Arumugam, Manimozhiyan, Atabaki-Pasdar, Naeimeh, Baltauss, Tania, Banasik, Karina, Barnett, Anna L., Baum, Patrick, Bell, Jimmy D., Beulens, Joline W., Bianzano, Susanna B., Bizzotto, Roberto, Bonnefond, Amelie, Cabrelli, Louise, Dale, Matilda, Dawed, Adem Y., de Preville, Nathalie, Dekkers, Koen F., Deshmukh, Harshal A., Dings, Christiane, Donnelly, Louise, Dutta, Avirup, Ehrhardt, Beate, Engelbrechtsen, Line, Eriksen, Rebeca, Fan, Yong, Ferrer, Jorge, Fitipaldi, Hugo, Forman, Annemette, Fritsche, Andreas, Froguel, Philippe, Gassenhuber, Johann, Gough, Stephen, Graefe-Mody, Ulrike, Grempler, Rolf, Groeneveld, Lenka, Groop, Leif, Gudmundsdóttir, Valborg, Gupta, Ramneek, Hennige, Anita M.H., Hill, Anita V., Holl, Reinhard W., Hudson, Michelle, Jacobsen, Ulrik Plesner, Jennison, Christopher, Johansen, Joachim, Jonsson, Anna, Karaderi, Tugce, Kaye, Jane, Kennedy, Gwen, Klintenberg, Maria, Kuulasmaa, Teemu, Lehr, Thorsten, Loftus, Heather, Lundgaard, Agnete Troen T., Mazzoni, Gianluca, McRobert, Nicky, McVittie, Ian, Nice, Rachel, Nicolay, Claudia, Nijpels, Giel, Palmer, Colin N., Pedersen, Helle K., Perry, Mandy H., Pomares-Millan, Hugo, Prehn, Cornelia P., Ramisch, Anna, Rasmussen, Simon, Robertson, Neil, Rodriquez, Marianne, Sackett, Peter, Scherer, Nina, Shah, Nisha, Sihinevich, Iryna, Slieker, Roderick C., Sondertoft, Nadja B., Steckel-Hamann, Birgit, Thomas, Melissa K., Thomas, Cecilia Engel E., Thomas, Elizabeth Louise L., Thorand, Barbara, Thorne, Claire E., Tillner, Joachim, Tura, Andrea, Uhlen, Mathias, van Leeuwen, Nienke, van Oort, Sabine, Verkindt, Helene, Vogt, Josef, Wad Sackett, Peter W., Wesolowska-Andersen, Agata, Whitcher, Brandon, White, Margaret W., Adamski, Jerzy, Schwenk, Jochen M., Pearson, Ewan R., Dermitzakis, Emmanouil T., and Viñuela, Ana

Abstract

We evaluate the shared genetic regulation of mRNA molecules, proteins and metabolites derived from whole blood from 3029 human donors. We find abundant allelic heterogeneity, where multiple variants regulate a particular molecular phenotype, and pleiotropy, where a single variant associates with multiple molecular phenotypes over multiple genomic regions. The highest proportion of share genetic regulation is detected between gene expression and proteins (66.6%), with a further median shared genetic associations across 49 different tissues of 78.3% and 62.4% between plasma proteins and gene expression. We represent the genetic and molecular associations in networks including 2828 known GWAS variants, showing that GWAS variants are more often connected to gene expression in trans than other molecular phenotypes in the network. Our work provides a roadmap to understanding molecular networks and deriving the underlying mechanism of action of GWAS variants using different molecular phenotypes in an accessible tissue.

Published
2023

9. Development and validation of an interpretable machine learning-based calculator for predicting 5-year weight trajectories after bariatric surgery: a multinational retrospective cohort SOPHIA study

Author

Saux, Patrick, Bauvin, Pierre, Raverdy, Violeta, Teigny, Julien, Verkindt, Hélène, Soumphonphakdy, Tomy, Debert, Maxence, Jacobs, Anne, Jacobs, Daan, Monpellier, Valerie, Lee, Phong Ching, Lim, Chin Hong, Andersson-Assarsson, Johanna C, Carlsson, Lena, Svensson, Per-Arne, Galtier, Florence, Dezfoulian, Guelareh, Moldovanu, Mihaela, Andrieux, Severine, Couster, Julien, Lepage, Marie, Lembo, Erminia, Verrastro, Ornella, Robert, Maud, Salminen, Paulina, Mingrone, Geltrude, Peterli, Ralph, Cohen, Ricardo V, Zerrweck, Carlos, Nocca, David, Roux, Carel W Le, Caiazzo, Robert, Preux, Philippe, Pattou, François, Saux, Patrick, Bauvin, Pierre, Raverdy, Violeta, Teigny, Julien, Verkindt, Hélène, Soumphonphakdy, Tomy, Debert, Maxence, Jacobs, Anne, Jacobs, Daan, Monpellier, Valerie, Lee, Phong Ching, Lim, Chin Hong, Andersson-Assarsson, Johanna C, Carlsson, Lena, Svensson, Per-Arne, Galtier, Florence, Dezfoulian, Guelareh, Moldovanu, Mihaela, Andrieux, Severine, Couster, Julien, Lepage, Marie, Lembo, Erminia, Verrastro, Ornella, Robert, Maud, Salminen, Paulina, Mingrone, Geltrude, Peterli, Ralph, Cohen, Ricardo V, Zerrweck, Carlos, Nocca, David, Roux, Carel W Le, Caiazzo, Robert, Preux, Philippe, and Pattou, François

Abstract

Background Weight loss trajectories after bariatric surgery vary widely between individuals, and predicting weight loss before the operation remains challenging. We aimed to develop a model using machine learning to provide individual preoperative prediction of 5-year weight loss trajectories after surgery. Methods In this multinational retrospective observational study we enrolled adult participants (aged $\ge$18 years) from ten prospective cohorts (including ABOS [NCT01129297], BAREVAL [NCT02310178], the Swedish Obese Subjects study, and a large cohort from the Dutch Obesity Clinic [Nederlandse Obesitas Kliniek]) and two randomised trials (SleevePass [NCT00793143] and SM-BOSS [NCT00356213]) in Europe, the Americas, and Asia, with a 5 year followup after Roux-en-Y gastric bypass, sleeve gastrectomy, or gastric band. Patients with a previous history of bariatric surgery or large delays between scheduled and actual visits were excluded. The training cohort comprised patients from two centres in France (ABOS and BAREVAL). The primary outcome was BMI at 5 years. A model was developed using least absolute shrinkage and selection operator to select variables and the classification and regression trees algorithm to build interpretable regression trees. The performances of the model were assessed through the median absolute deviation (MAD) and root mean squared error (RMSE) of BMI. Findings10 231 patients from 12 centres in ten countries were included in the analysis, corresponding to 30 602 patient-years. Among participants in all 12 cohorts, 7701 (75$\bullet$3%) were female, 2530 (24$\bullet$7%) were male. Among 434 baseline attributes available in the training cohort, seven variables were selected: height, weight, intervention type, age, diabetes status, diabetes duration, and smoking status. At 5 years, across external testing cohorts the overall mean MAD BMI was 2$\bullet$8 kg/m${}^2$ (95% CI 2$\bullet$6-3$\bullet$0) and mean RMSE BMI was 4$\bullet$7 kg/m${}^2$ (4$\bu, Comment: The Lancet Digital Health, 2023

Published
2023
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10. Acyl-CoA-binding protein is a lipogenic factor that triggers food intake and obesity

Author

Ligue Nationale contre le Cancer (France), Agence Nationale de la Recherche (France), Cancéropôle Île-de-France, Chancellerie des Universités de Paris, European Research Council, Institut National du Cancer (France), Institut Universitaire de France, Fondation Leducq, Swedish Research Council, Austrian Science Fund, Federal Ministry of Science, Research and Economy (Austria), Bravo-San-Pedro, José Manuel [0000-0002-5781-1133], Sica, Valentina [0000-0003-2770-5847], Martins, Isabelle [0000-0003-0885-613X], Pol, Jonathan G. [0000-0002-8355-7562], Loos, Friedemann [0000-0002-5976-5978], Maiuri, Maria Chiara [0000-0001-9760-7674], Niso-Santano, Mireia [0000-0002-6506-422X], Aranda, Fernando [0000-0002-9364-474X], Gorwood, Philip [0000-0003-1845-3676], Ramoz, Nicolas [0000-0002-8070-9938], Cleḿent, Karine [0000-0002-2489-3355], Pelloux, Véronique [0000-0003-3630-4746], Raverdy, Violeta [0000-0001-5754-2028], Denis, Raphaël G.P. [0000-0002-7677-7460], Boya, Patricia [0000-0003-3045-951X], Galluzzi, Lorenzo [0000-0003-2257-8500 ], Madeo, Frank [0000-0002-5070-1329], Cruciani-Guglielmacci, Céline [0000-0002-2562-5360], Tavernarakis, Nektarios [0000-0002-5253-1466 ], López-Otín, Carlos [0000-0001-6964-1904], Magnan, Christophe [0000-0002-7044-2571], Kroemer, Guido [0000-0002-9334-4405], Bravo-San-Pedro, José Manuel, Sica, Valentina, Martins, Isabelle, Pol, Jonathan G., Loos, Friedemann, Maiuri, Maria Chiara, Durand, Sylvère, Bossut, Noélie, Aprahamian, Fanny, Anagnostopoulos, Gerasimos, Niso-Santano, Mireia, Aranda, Fernando, Ramírez-Pardo, Ignacio, Lallement, Justine, Denom, Jessica, Boedec, Erwan, Gorwood, Philip, Ramoz, Nicolas, Cleḿent, Karine, Pelloux, Véronique, Rohia, Alili, Pattou, François, Raverdy, Violeta, Caïazzo, Robert, Denis, Raphaël G.P., Boya, Patricia, Galluzzi, Lorenzo, Madeo, Frank, Migrenne-Li, Stephanie, Cruciani-Guglielmacci, Céline, Tavernarakis, Nektarios, López-Otín, Carlos, Magnan, Christophe, Kroemer, Guido, Ligue Nationale contre le Cancer (France), Agence Nationale de la Recherche (France), Cancéropôle Île-de-France, Chancellerie des Universités de Paris, European Research Council, Institut National du Cancer (France), Institut Universitaire de France, Fondation Leducq, Swedish Research Council, Austrian Science Fund, Federal Ministry of Science, Research and Economy (Austria), Bravo-San-Pedro, José Manuel [0000-0002-5781-1133], Sica, Valentina [0000-0003-2770-5847], Martins, Isabelle [0000-0003-0885-613X], Pol, Jonathan G. [0000-0002-8355-7562], Loos, Friedemann [0000-0002-5976-5978], Maiuri, Maria Chiara [0000-0001-9760-7674], Niso-Santano, Mireia [0000-0002-6506-422X], Aranda, Fernando [0000-0002-9364-474X], Gorwood, Philip [0000-0003-1845-3676], Ramoz, Nicolas [0000-0002-8070-9938], Cleḿent, Karine [0000-0002-2489-3355], Pelloux, Véronique [0000-0003-3630-4746], Raverdy, Violeta [0000-0001-5754-2028], Denis, Raphaël G.P. [0000-0002-7677-7460], Boya, Patricia [0000-0003-3045-951X], Galluzzi, Lorenzo [0000-0003-2257-8500 ], Madeo, Frank [0000-0002-5070-1329], Cruciani-Guglielmacci, Céline [0000-0002-2562-5360], Tavernarakis, Nektarios [0000-0002-5253-1466 ], López-Otín, Carlos [0000-0001-6964-1904], Magnan, Christophe [0000-0002-7044-2571], Kroemer, Guido [0000-0002-9334-4405], Bravo-San-Pedro, José Manuel, Sica, Valentina, Martins, Isabelle, Pol, Jonathan G., Loos, Friedemann, Maiuri, Maria Chiara, Durand, Sylvère, Bossut, Noélie, Aprahamian, Fanny, Anagnostopoulos, Gerasimos, Niso-Santano, Mireia, Aranda, Fernando, Ramírez-Pardo, Ignacio, Lallement, Justine, Denom, Jessica, Boedec, Erwan, Gorwood, Philip, Ramoz, Nicolas, Cleḿent, Karine, Pelloux, Véronique, Rohia, Alili, Pattou, François, Raverdy, Violeta, Caïazzo, Robert, Denis, Raphaël G.P., Boya, Patricia, Galluzzi, Lorenzo, Madeo, Frank, Migrenne-Li, Stephanie, Cruciani-Guglielmacci, Céline, Tavernarakis, Nektarios, López-Otín, Carlos, Magnan, Christophe, and Kroemer, Guido

Abstract

Autophagy facilitates the adaptation to nutritional stress. Here, we show that short-term starvation of cultured cells or mice caused the autophagy-dependent cellular release of acyl-CoA-binding protein (ACBP, also known as diazepam-binding inhibitor, DBI) and consequent ACBP-mediated feedback inhibition of autophagy. Importantly, ACBP levels were elevated in obese patients and reduced in anorexia nervosa. In mice, systemic injection of ACBP protein inhibited autophagy, induced lipogenesis, reduced glycemia, and stimulated appetite as well as weight gain. We designed three approaches to neutralize ACBP, namely, inducible whole-body knockout, systemic administration of neutralizing antibodies, and induction of antiACBP autoantibodies in mice. ACBP neutralization enhanced autophagy, stimulated fatty acid oxidation, inhibited appetite, reduced weight gain in the context of a high-fat diet or leptin deficiency, and accelerated weight loss in response to dietary changes. In conclusion, neutralization of ACBP might constitute a strategy for treating obesity and its co-morbidities.

Published
2019

11. BMI and pneumonia outcomes in critically ill COVID-19 patients : an international multicenter study

Author

Chetboun, Mikael, Raverdy, Violeta, Labreuche, Julien, Simonnet, Arthur, Wallet, Florent, Caussy, Cyrielle, Antonelli, Massimo, Artigas, Antonio, Goma, Gemma, Meziani, Ferhat, Helms, Julie, Mylonakis, Eleftherios, Levy, Mitchell M., Kalligeros, Markos, Latronico, Nicola, Piva, Simone, Cerf, Charles, Neuville, Mathilde, Klouche, Kada, Larcher, Romaric, Tamion, Fabienne, Occhiali, Emilie, Snacken, Morgane, Preiser, Jean-Charles, Kontar, Loay, Riviere, Antoine, Silva, Stein, Sarton, Benjamine, Krouchi, Raphael, Dubar, Victoria, Palaiodimos, Leonidas, Karamanis, Dimitrios, Perche, Juliette, L'her, Erwan, Busetto, Luca, Dicker, Dror, Lev, Shaul, Duhamel, Alain, Jourdain, Mercè, Pattou, François, Chetboun, Mikael, Raverdy, Violeta, Labreuche, Julien, Simonnet, Arthur, Wallet, Florent, Caussy, Cyrielle, Antonelli, Massimo, Artigas, Antonio, Goma, Gemma, Meziani, Ferhat, Helms, Julie, Mylonakis, Eleftherios, Levy, Mitchell M., Kalligeros, Markos, Latronico, Nicola, Piva, Simone, Cerf, Charles, Neuville, Mathilde, Klouche, Kada, Larcher, Romaric, Tamion, Fabienne, Occhiali, Emilie, Snacken, Morgane, Preiser, Jean-Charles, Kontar, Loay, Riviere, Antoine, Silva, Stein, Sarton, Benjamine, Krouchi, Raphael, Dubar, Victoria, Palaiodimos, Leonidas, Karamanis, Dimitrios, Perche, Juliette, L'her, Erwan, Busetto, Luca, Dicker, Dror, Lev, Shaul, Duhamel, Alain, Jourdain, Mercè, and Pattou, François

Abstract

Previous studies unveiled a relation between the severity of COVID-19 pneumonia and obesity. The aims of this multicenter retrospective cohort study were to disentangle the association of BMI and associated metabolic risk factors (diabetes, hypertension, hyperlipidemia, current smoking) in critically ill patients with COVID-19. This multicenter retrospective cohort study enrolled patients admitted in intensive care for COVID-19, in 21 centers (Europe, Israel, USA) between 02/19/2020 and 05/19/2020. Primary and secondary outcomes were the need for invasive mechanical ventilation (IMV), and 28-day mortality. A total of 1,461 patients were enrolled, median(IQR) age was 64 years (40.9-72.0); 73.2% males; BMI 28.1 kg/m 2 (25.4-32.3); 1,080 patients (73.9%) required IMV; the 28-day mortality estimate was 36.1% (95%CI, 33.0-39.5). Adjusted mixed logistic regression model showed a significant linear relation between BMI and IMV: OR 1.27 (95%CI, 1.12-1.45) per 5 kg/m 2. Adjusted Cox proportional hazards regression model showed a significant association between BMI and mortality, which was only increased in obesity class III (≥40 kg/m 2) (HR 1.68 (95%CI 1.06-2.64). In critically ill COVID-19 patients, we observed a linear association between BMI and the need for IMV, independent of other metabolic risk factors, and a non-linear association between BMI and mortality risk. (NCT04391738).

Published
2021

12. 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., Louise Thomas, E., 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, de Masi, Federico, 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, Franks, Paul W., Atabaki-Pasdar, Naeimeh, Ohlsson, Mattias, Viñuela, Ana, Frau, Francesca, Pomares-Millan, Hugo, Haid, Mark, Jones, Angus G., Louise Thomas, E., 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, de Masi, Federico, 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.

Abstract

BackgroundNon-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 findingsWe 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. Anot

Published
2020

13. 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, Pedersen, Oluf, 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 (<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 lim

Published
2020

14. 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, Pedersen, Oluf, 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 (<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 lim

Published
2020

15. The histone demethylase Phf2 acts as a molecular checkpoint to prevent NAFLD progression during obesity.

Author

Bricambert, Julien, Bricambert, Julien, Alves-Guerra, Marie-Clotilde, Esteves, Pauline, Prip-Buus, Carina, Bertrand-Michel, Justine, Guillou, Hervé, Chang, Christopher J, Vander Wal, Mark N, Canonne-Hergaux, François, Mathurin, Philippe, Raverdy, Violeta, Pattou, François, Girard, Jean, Postic, Catherine, Dentin, Renaud, Bricambert, Julien, Bricambert, Julien, Alves-Guerra, Marie-Clotilde, Esteves, Pauline, Prip-Buus, Carina, Bertrand-Michel, Justine, Guillou, Hervé, Chang, Christopher J, Vander Wal, Mark N, Canonne-Hergaux, François, Mathurin, Philippe, Raverdy, Violeta, Pattou, François, Girard, Jean, Postic, Catherine, and Dentin, Renaud

Abstract

Aberrant histone methylation profile is reported to correlate with the development and progression of NAFLD during obesity. However, the identification of specific epigenetic modifiers involved in this process remains poorly understood. Here, we identify the histone demethylase Plant Homeodomain Finger 2 (Phf2) as a new transcriptional co-activator of the transcription factor Carbohydrate Responsive Element Binding Protein (ChREBP). By specifically erasing H3K9me2 methyl-marks on the promoter of ChREBP-regulated genes, Phf2 facilitates incorporation of metabolic precursors into mono-unsaturated fatty acids, leading to hepatosteatosis development in the absence of inflammation and insulin resistance. Moreover, the Phf2-mediated activation of the transcription factor NF-E2-related factor 2 (Nrf2) further reroutes glucose fluxes toward the pentose phosphate pathway and glutathione biosynthesis, protecting the liver from oxidative stress and fibrogenesis in response to diet-induced obesity. Overall, our findings establish a downstream epigenetic checkpoint, whereby Phf2, through facilitating H3K9me2 demethylation at specific gene promoters, protects liver from the pathogenesis progression of NAFLD.

Published
2018

16. The histone demethylase Phf2 acts as a molecular checkpoint to prevent NAFLD progression during obesity.

Author

Bricambert, Julien, Bricambert, Julien, Alves-Guerra, Marie-Clotilde, Esteves, Pauline, Prip-Buus, Carina, Bertrand-Michel, Justine, Guillou, Hervé, Chang, Christopher J, Vander Wal, Mark N, Canonne-Hergaux, François, Mathurin, Philippe, Raverdy, Violeta, Pattou, François, Girard, Jean, Postic, Catherine, Dentin, Renaud, Bricambert, Julien, Bricambert, Julien, Alves-Guerra, Marie-Clotilde, Esteves, Pauline, Prip-Buus, Carina, Bertrand-Michel, Justine, Guillou, Hervé, Chang, Christopher J, Vander Wal, Mark N, Canonne-Hergaux, François, Mathurin, Philippe, Raverdy, Violeta, Pattou, François, Girard, Jean, Postic, Catherine, and Dentin, Renaud

Abstract

Aberrant histone methylation profile is reported to correlate with the development and progression of NAFLD during obesity. However, the identification of specific epigenetic modifiers involved in this process remains poorly understood. Here, we identify the histone demethylase Plant Homeodomain Finger 2 (Phf2) as a new transcriptional co-activator of the transcription factor Carbohydrate Responsive Element Binding Protein (ChREBP). By specifically erasing H3K9me2 methyl-marks on the promoter of ChREBP-regulated genes, Phf2 facilitates incorporation of metabolic precursors into mono-unsaturated fatty acids, leading to hepatosteatosis development in the absence of inflammation and insulin resistance. Moreover, the Phf2-mediated activation of the transcription factor NF-E2-related factor 2 (Nrf2) further reroutes glucose fluxes toward the pentose phosphate pathway and glutathione biosynthesis, protecting the liver from oxidative stress and fibrogenesis in response to diet-induced obesity. Overall, our findings establish a downstream epigenetic checkpoint, whereby Phf2, through facilitating H3K9me2 demethylation at specific gene promoters, protects liver from the pathogenesis progression of NAFLD.

Published
2018

17. Low copy number of the salivary amylase gene predisposes to obesity.

Author

Falchi, Mario, Falchi, Mario, El-Sayed Moustafa, Julia Sarah, Takousis, Petros, Pesce, Francesco, Bonnefond, Amélie, Andersson-Assarsson, Johanna C, Sudmant, Peter H, Dorajoo, Rajkumar, Al-Shafai, Mashael Nedham, Bottolo, Leonardo, Ozdemir, Erdal, So, Hon-Cheong, Davies, Robert W, Patrice, Alexandre, Dent, Robert, Mangino, Massimo, Hysi, Pirro G, Dechaume, Aurélie, Huyvaert, Marlène, Skinner, Jane, Pigeyre, Marie, Caiazzo, Robert, Raverdy, Violeta, Vaillant, Emmanuel, Field, Sarah, Balkau, Beverley, Marre, Michel, Visvikis-Siest, Sophie, Weill, Jacques, Poulain-Godefroy, Odile, Jacobson, Peter, Sjostrom, Lars, Hammond, Christopher J, Deloukas, Panos, Sham, Pak Chung, McPherson, Ruth, Lee, Jeannette, Tai, E Shyong, Sladek, Robert, Carlsson, Lena MS, Walley, Andrew, Eichler, Evan E, Pattou, Francois, Spector, Timothy D, Froguel, Philippe, Falchi, Mario, Falchi, Mario, El-Sayed Moustafa, Julia Sarah, Takousis, Petros, Pesce, Francesco, Bonnefond, Amélie, Andersson-Assarsson, Johanna C, Sudmant, Peter H, Dorajoo, Rajkumar, Al-Shafai, Mashael Nedham, Bottolo, Leonardo, Ozdemir, Erdal, So, Hon-Cheong, Davies, Robert W, Patrice, Alexandre, Dent, Robert, Mangino, Massimo, Hysi, Pirro G, Dechaume, Aurélie, Huyvaert, Marlène, Skinner, Jane, Pigeyre, Marie, Caiazzo, Robert, Raverdy, Violeta, Vaillant, Emmanuel, Field, Sarah, Balkau, Beverley, Marre, Michel, Visvikis-Siest, Sophie, Weill, Jacques, Poulain-Godefroy, Odile, Jacobson, Peter, Sjostrom, Lars, Hammond, Christopher J, Deloukas, Panos, Sham, Pak Chung, McPherson, Ruth, Lee, Jeannette, Tai, E Shyong, Sladek, Robert, Carlsson, Lena MS, Walley, Andrew, Eichler, Evan E, Pattou, Francois, Spector, Timothy D, and Froguel, Philippe

Abstract

Common multi-allelic copy number variants (CNVs) appear enriched for phenotypic associations compared to their biallelic counterparts. Here we investigated the influence of gene dosage effects on adiposity through a CNV association study of gene expression levels in adipose tissue. We identified significant association of a multi-allelic CNV encompassing the salivary amylase gene (AMY1) with body mass index (BMI) and obesity, and we replicated this finding in 6,200 subjects. Increased AMY1 copy number was positively associated with both amylase gene expression (P = 2.31 × 10(-14)) and serum enzyme levels (P < 2.20 × 10(-16)), whereas reduced AMY1 copy number was associated with increased BMI (change in BMI per estimated copy = -0.15 (0.02) kg/m(2); P = 6.93 × 10(-10)) and obesity risk (odds ratio (OR) per estimated copy = 1.19, 95% confidence interval (CI) = 1.13-1.26; P = 1.46 × 10(-10)). The OR value of 1.19 per copy of AMY1 translates into about an eightfold difference in risk of obesity between subjects in the top (copy number > 9) and bottom (copy number < 4) 10% of the copy number distribution. Our study provides a first genetic link between carbohydrate metabolism and BMI and demonstrates the power of integrated genomic approaches beyond genome-wide association studies.

Published
2014

18. Low copy number of the salivary amylase gene predisposes to obesity.

Author

Falchi, Mario, Falchi, Mario, El-Sayed Moustafa, Julia Sarah, Takousis, Petros, Pesce, Francesco, Bonnefond, Amélie, Andersson-Assarsson, Johanna C, Sudmant, Peter H, Dorajoo, Rajkumar, Al-Shafai, Mashael Nedham, Bottolo, Leonardo, Ozdemir, Erdal, So, Hon-Cheong, Davies, Robert W, Patrice, Alexandre, Dent, Robert, Mangino, Massimo, Hysi, Pirro G, Dechaume, Aurélie, Huyvaert, Marlène, Skinner, Jane, Pigeyre, Marie, Caiazzo, Robert, Raverdy, Violeta, Vaillant, Emmanuel, Field, Sarah, Balkau, Beverley, Marre, Michel, Visvikis-Siest, Sophie, Weill, Jacques, Poulain-Godefroy, Odile, Jacobson, Peter, Sjostrom, Lars, Hammond, Christopher J, Deloukas, Panos, Sham, Pak Chung, McPherson, Ruth, Lee, Jeannette, Tai, E Shyong, Sladek, Robert, Carlsson, Lena MS, Walley, Andrew, Eichler, Evan E, Pattou, Francois, Spector, Timothy D, Froguel, Philippe, Falchi, Mario, Falchi, Mario, El-Sayed Moustafa, Julia Sarah, Takousis, Petros, Pesce, Francesco, Bonnefond, Amélie, Andersson-Assarsson, Johanna C, Sudmant, Peter H, Dorajoo, Rajkumar, Al-Shafai, Mashael Nedham, Bottolo, Leonardo, Ozdemir, Erdal, So, Hon-Cheong, Davies, Robert W, Patrice, Alexandre, Dent, Robert, Mangino, Massimo, Hysi, Pirro G, Dechaume, Aurélie, Huyvaert, Marlène, Skinner, Jane, Pigeyre, Marie, Caiazzo, Robert, Raverdy, Violeta, Vaillant, Emmanuel, Field, Sarah, Balkau, Beverley, Marre, Michel, Visvikis-Siest, Sophie, Weill, Jacques, Poulain-Godefroy, Odile, Jacobson, Peter, Sjostrom, Lars, Hammond, Christopher J, Deloukas, Panos, Sham, Pak Chung, McPherson, Ruth, Lee, Jeannette, Tai, E Shyong, Sladek, Robert, Carlsson, Lena MS, Walley, Andrew, Eichler, Evan E, Pattou, Francois, Spector, Timothy D, and Froguel, Philippe

Abstract

Common multi-allelic copy number variants (CNVs) appear enriched for phenotypic associations compared to their biallelic counterparts. Here we investigated the influence of gene dosage effects on adiposity through a CNV association study of gene expression levels in adipose tissue. We identified significant association of a multi-allelic CNV encompassing the salivary amylase gene (AMY1) with body mass index (BMI) and obesity, and we replicated this finding in 6,200 subjects. Increased AMY1 copy number was positively associated with both amylase gene expression (P = 2.31 × 10(-14)) and serum enzyme levels (P < 2.20 × 10(-16)), whereas reduced AMY1 copy number was associated with increased BMI (change in BMI per estimated copy = -0.15 (0.02) kg/m(2); P = 6.93 × 10(-10)) and obesity risk (odds ratio (OR) per estimated copy = 1.19, 95% confidence interval (CI) = 1.13-1.26; P = 1.46 × 10(-10)). The OR value of 1.19 per copy of AMY1 translates into about an eightfold difference in risk of obesity between subjects in the top (copy number > 9) and bottom (copy number < 4) 10% of the copy number distribution. Our study provides a first genetic link between carbohydrate metabolism and BMI and demonstrates the power of integrated genomic approaches beyond genome-wide association studies.

Published
2014

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