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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, and Brunak, Søren

Published
2023
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3. Microbially Produced Imidazole Propionate Is Associated With Heart Failure and Mortality

Author

Alves, Renato, Amouyal, Chloe, Andersson Galijatovic, Ehm Astrid, Andreelli, Fabrizio, Barthelemy, Olivier, Bastard, Jean-Philippe, Batisse, Jean-Paul, Berland, Magalie, Bittar, Randa, Blüher, Matthias, Bork, Peer, Bourron, Olivier, Camus, Mickael, Cassuto, Dominique, Ciangura, Cecile, Coelho, Luis Pedro, Collet, Jean-Philippe, Dumas, Marc-Emmanuel, Ehrlich, S. Dusko, Engelbrechtsen, Line, Fezeu, Leopold, Forslund, Sofia, Fromentin, Sebastien, Galan, Pilar, Giral, Philippe, Gøtze, Jens Peter, Hansen, Torben, Hansen, Tue H., Hartemann, Agnes, Hartmann, Bolette, Hercberg, Serge, Holmes, Bridget, Holst, Jens Juul, Hornbak, Malene, Hoyles, Lesley, Hulot, Jean-Sebastien, Jaqueminet, Sophie, Kerneis, Mathieu, Khemis, Jean, Kozlowski, Ruby, Pedersen, Helle Krogh, Kuhn, Michael, Mannerås-Holm, Louise, Marko, Lajos, Martinez-Gili Robin Massey, Laura, Maziers, Nicolas, Medina-Stamminger, Jonathan, Moitinho-Silva, Lucas, Montalescot, Gilles, Moutel, Sandrine, Neves, Ana Luisa, Olanipekun, Michael, Oppert, Jean-Michel, Poitou, Christine, Pousset, Francoise, Pouzoulet, Laurence, Rouault, Christine, Silvain, Johanne, Vestergaard, Henrik, Molinaro, Antonio, Nemet, Ina, Bel Lassen, Pierre, Chakaroun, Rima, Nielsen, Trine, Aron-Wisnewsky, Judith, Bergh, Per-Olof, Li, Lin, Henricsson, Marcus, Køber, Lars, Isnard, Richard, Helft, Gerard, Stumvoll, Michael, Pedersen, Oluf, Smith, J. Gustav, Tang, W.H. Wilson, Clément, Karine, Hazen, Stanley L., and Bäckhed, Fredrik

Published
2023
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4. 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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5. Microbiome and metabolome features of the cardiometabolic disease spectrum

Author

Fromentin, Sebastien, Forslund, Sofia K., Chechi, Kanta, Aron-Wisnewsky, Judith, Chakaroun, Rima, Nielsen, Trine, Tremaroli, Valentina, Ji, Boyang, Prifti, Edi, Myridakis, Antonis, Chilloux, Julien, Andrikopoulos, Petros, Fan, Yong, Olanipekun, Michael T., Alves, Renato, Adiouch, Solia, Bar, Noam, Talmor-Barkan, Yeela, Belda, Eugeni, Caesar, Robert, Coelho, Luis Pedro, Falony, Gwen, Fellahi, Soraya, Galan, Pilar, Galleron, Nathalie, Helft, Gerard, Hoyles, Lesley, Isnard, Richard, Le Chatelier, Emmanuelle, Julienne, Hanna, Olsson, Lisa, Pedersen, Helle Krogh, Pons, Nicolas, Quinquis, Benoit, Rouault, Christine, Roume, Hugo, Salem, Joe-Elie, Schmidt, Thomas S. B., Vieira-Silva, Sara, Li, Peishun, Zimmermann-Kogadeeva, Maria, Lewinter, Christian, Søndertoft, Nadja B., Hansen, Tue H., Gauguier, Dominique, Gøtze, Jens Peter, Køber, Lars, Kornowski, Ran, Vestergaard, Henrik, Hansen, Torben, Zucker, Jean-Daniel, Hercberg, Serge, Letunic, Ivica, Bäckhed, Fredrik, Oppert, Jean-Michel, Nielsen, Jens, Raes, Jeroen, Bork, Peer, Stumvoll, Michael, Segal, Eran, Clément, Karine, Dumas, Marc-Emmanuel, Ehrlich, S. Dusko, and Pedersen, Oluf

Published
2022
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6. Combinatorial, additive and dose-dependent drug–microbiome associations

Author

Forslund, Sofia K., Chakaroun, Rima, Zimmermann-Kogadeeva, Maria, Markó, Lajos, Aron-Wisnewsky, Judith, Nielsen, Trine, Moitinho-Silva, Lucas, Schmidt, Thomas S. B., Falony, Gwen, Vieira-Silva, Sara, Adriouch, Solia, Alves, Renato J., Assmann, Karen, Bastard, Jean-Philippe, Birkner, Till, Caesar, Robert, Chilloux, Julien, Coelho, Luis Pedro, Fezeu, Leopold, Galleron, Nathalie, Helft, Gerard, Isnard, Richard, Ji, Boyang, Kuhn, Michael, Le Chatelier, Emmanuelle, Myridakis, Antonis, Olsson, Lisa, Pons, Nicolas, Prifti, Edi, Quinquis, Benoit, Roume, Hugo, Salem, Joe-Elie, Sokolovska, Nataliya, Tremaroli, Valentina, Valles-Colomer, Mireia, Lewinter, Christian, Søndertoft, Nadja B., Pedersen, Helle Krogh, Hansen, Tue H., Gøtze, Jens Peter, Køber, Lars, Vestergaard, Henrik, Hansen, Torben, Zucker, Jean-Daniel, Hercberg, Serge, Oppert, Jean-Michel, Letunic, Ivica, Nielsen, Jens, Bäckhed, Fredrik, Ehrlich, S. Dusko, Dumas, Marc-Emmanuel, Raes, Jeroen, Pedersen, Oluf, Clément, Karine, Stumvoll, Michael, and Bork, Peer

Published
2021
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8. 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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10. 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

11. Human gut microbes impact host serum metabolome and insulin sensitivity

Author

Pedersen, Helle Krogh, Gudmundsdottir, Valborg, Nielsen, Henrik Bjorn, Hyotylainen, Tuulia, Nielsen, Trine, Jensen, Benjamin A.H., Forslund, Kristoffer, Hildebrand, Falk, Prifti, Edi, Falony, Gwen, Le Chatelier, Emmanuelle, Levenez, Florence, Dore, Joel, Mattila, Ismo, Plichta, Damian R., Poho, Paivi, Hellgren, Lars I., Arumugam, Manimozhiyan, Sunagawa, Shinichi, Vieira-Silva, Sara, Jorgensen, Torben, Holm, Jacob Bak, Trost, Kajetan, Kristiansen, Karsten, Brix, Susanne, Raes, Jeroen, Wang, Jun, Hansen, Torben, Bork, Peer, Brunak, Soren, Oresic, Matej, Ehrlich, S. Dusko, and Pedersen, Oluf

Subjects
Insulin resistance -- Genetic aspects, Amino acids, Metabolic diseases, Cardiovascular diseases, Environmental issues, Science and technology, Zoology and wildlife conservation
Abstract

Insulin resistance is a forerunner state of ischaemic cardiovascular disease and type 2 diabetes. Here we show how the human gut microbiome impacts the serum metabolome and associates with insulin resistance in 277 non-diabetic Danish individuals. The serum metabolome of insulin-resistant individuals is characterized by increased levels of branched- chain amino acids (BCAAs), which correlate with a gut microbiome that has an enriched biosynthetic potential for BCAAs and is deprived of genes encoding bacterial inward transporters for these amino acids. Prevotella copri and Bacteroides vulgatus are identified as the main species driving the association between biosynthesis of BCAAs and insulin resistance, and in mice we demonstrate that P. copri can induce insulin resistance, aggravate glucose intolerance and augment circulating levels of BCAAs. Our findings suggest that microbial targets may have the potential to diminish insulin resistance and reduce the incidence of common metabolic and cardiovascular disorders., Insulin resistance (IR) and metabolic syndrome are risk factors for both type 2 diabetes and ischaemic cardiovascular diseases, pathologies that are in epidemic growth worldwide. Mounting evidence suggests a link [...]

Published
2016
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13. Impairment of gut microbial biotin metabolism and host biotin status in severe obesity:effect of biotin and prebiotic supplementation on improved metabolism

Author

Belda, Eugeni, Voland, Lise, Tremaroli, Valentina, Falony, Gwen, Adriouch, Solia, Assmann, Karen E., Prifiti, Edi, Aron-Wisnewsky, Judith, Debedat, Jean, Le Roy, Tiphaine, Nielsen, Trine, Amouyal, Chloe, Andre, Sebastien, Andreelli, Fabrizio, Blueher, Matthias, Chakaroun, Rima, Chilloux, Julien, Coelho, Luis Pedro, Dao, Maria Carlota, Das, Promi, Fellahi, Soraya, Forslund, Sofia, Galleron, Nathalie, Hansen, Tue H., Holmes, Bridget, Ji, Boyang, Pedersen, Helle Krogh, Phuong Le, Le Chatelier, Emmanuelle, Lewinter, Christian, Manneras-Holm, Louise, Marquet, Florian, Myridakis, Antonis, Pelloux, Veronique, Pons, Nicolas, Quinquis, Benoit, Rouault, Christine, Roume, Hugo, Salem, Joe-Elie, Sokolovska, Nataliya, Søndertoft, Nadja B., Touch, Sothea, Vieira-Silva, Sara, Galan, Pilar, Holst, Jens, Gøtze, Jens Peter, Køber, Lars, Vestergaard, Henrik, Hansen, Torben, Hercberg, Serge, Oppert, Jean-Michel, Nielsen, Jens, Letunic, Ivica, Dumas, Marc-Emmanuel, Stumvoll, Michael, Pedersen, Oluf Borbye, Bork, Peer, Ehrlich, Stanislav Dusko, Zucker, Jean-Daniel, Baeckhed, Fredrik, Raes, Jeroen, Clement, Karine, Belda, Eugeni, Voland, Lise, Tremaroli, Valentina, Falony, Gwen, Adriouch, Solia, Assmann, Karen E., Prifiti, Edi, Aron-Wisnewsky, Judith, Debedat, Jean, Le Roy, Tiphaine, Nielsen, Trine, Amouyal, Chloe, Andre, Sebastien, Andreelli, Fabrizio, Blueher, Matthias, Chakaroun, Rima, Chilloux, Julien, Coelho, Luis Pedro, Dao, Maria Carlota, Das, Promi, Fellahi, Soraya, Forslund, Sofia, Galleron, Nathalie, Hansen, Tue H., Holmes, Bridget, Ji, Boyang, Pedersen, Helle Krogh, Phuong Le, Le Chatelier, Emmanuelle, Lewinter, Christian, Manneras-Holm, Louise, Marquet, Florian, Myridakis, Antonis, Pelloux, Veronique, Pons, Nicolas, Quinquis, Benoit, Rouault, Christine, Roume, Hugo, Salem, Joe-Elie, Sokolovska, Nataliya, Søndertoft, Nadja B., Touch, Sothea, Vieira-Silva, Sara, Galan, Pilar, Holst, Jens, Gøtze, Jens Peter, Køber, Lars, Vestergaard, Henrik, Hansen, Torben, Hercberg, Serge, Oppert, Jean-Michel, Nielsen, Jens, Letunic, Ivica, Dumas, Marc-Emmanuel, Stumvoll, Michael, Pedersen, Oluf Borbye, Bork, Peer, Ehrlich, Stanislav Dusko, Zucker, Jean-Daniel, Baeckhed, Fredrik, Raes, Jeroen, and Clement, Karine

Published
2022

14. Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota

Author

Forslund, Kristoffer, Hildebrand, Falk, Nielsen, Trine, Falony, Gwen, Le Chatelier, Emmanuelle, Sunagawa, Shinichi, Prifti, Edi, Vieira-Silva, Sara, Gudmundsdottir, Valborg, Pedersen, Helle Krogh, Arumugam, Manimozhiyan, Kristiansen, Karsten, Voigt, Anita Yvonne, Vestergaard, Henrik, Hercog, Rajna, Costea, Paul Igor, Kultima, Jens Roat, Li, Junhua, Jorgensen, Torben, Levenez, Florence, Dore, Joel, Nielsen, H. Bjorn, Brunak, Soren, Raes, Jeroen, Hansen, Torben, Wang, Jun, Ehrlich, S. Dusko, Bork, Peer, and Pedersen, Oluf

Subjects
Microbiota (Symbiotic organisms) -- Physiological aspects, Diabetes therapy -- Physiological aspects, Metformin -- Patient outcomes -- Physiological aspects, Type 2 diabetes -- Drug therapy -- Physiological aspects -- Patient outcomes, Environmental issues, Science and technology, Zoology and wildlife conservation, Drug therapy, Physiological aspects, Patient outcomes
Abstract

In recent years, several associations between common chronic human disorders and altered gut microbiome composition and function have been reported (1, 2). In most of these reports, treatment regimens were not controlled for and conclusions could thus be confounded by the effects of various drugs on the microbiota, which may obscure microbial causes, protective factors or diagnostically relevant signals. Our study addresses disease and drug signatures in the human gut microbiome of type 2 diabetes mellitus (T2D). Two previous quantitative gut metagenomics studies of T2D patients that were unstratified for treatment yielded divergent conclusions regarding its associated gut microbial dysbiosis (3, 4). Here we show, using 784 available human gut metagenomes, how antidiabetic medication confounds these results, and analyse in detail the effects of the most widely used antidiabetic drug metformin. We provide support for microbial mediation of the therapeutic effects of metformin through short-chain fatty acid production, as well as for potential microbiota-mediated mechanisms behind known intestinal adverse effects in the form of a relative increase in abundance of Escherichia species. Controlling for metformin treatment, we report a unified signature of gut microbiome shifts in T2D with a depletion of butyrate-producing taxa (3, 4). These in turn cause functional microbiome shifts, in part alleviated by metformin-induced changes. Overall, the present study emphasizes the need to disentangle gut microbiota signatures of specific human diseases from those of medication., T2D is a disorder of elevated blood glucose levels (hyperglycaemia) primarily due to insulin resistance and inadequate insulin secretion, with rising global prevalence. Genetic and environmental risk factors are known, [...]

Published
2015

15. Effects of an Amino Acid-Based Formula Supplemented with Two Human Milk Oligosaccharides on Growth, Tolerability, Safety, and Gut Microbiome in Infants with Cow’s Milk Protein Allergy

Author

Gold, Michael S., primary, Quinn, Patrick J., additional, Campbell, Dianne E., additional, Peake, Jane, additional, Smart, Joanne, additional, Robinson, Marnie, additional, O’Sullivan, Michael, additional, Vogt, Josef Korbinian, additional, Pedersen, Helle Krogh, additional, Liu, Xiaoqiu, additional, Pazirandeh-Micol, Elham, additional, and Heine, Ralf G., additional

Published
2022
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16. Microbially Produced Imidazole Propionate Is Associated With Heart Failure and Mortality

Author

Molinaro, Antonio, Nemet, Ina, Bel Lassen, Pierre, Chakaroun, Rima, Nielsen, Trine, Aron-Wisnewsky, Judith, Bergh, Per-Olof, Li, Lin, Henricsson, Marcus, Køber, Lars, Isnard, Richard, Helft, Gerard, Stumvoll, Michael, Pedersen, Oluf, Smith, J. Gustav, Tang, W.H. Wilson, Clément, Karine, Hazen, Stanley L., Bäckhed, Fredrik, Alves, Renato, Amouyal, Chloe, Andersson Galijatovic, Ehm Astrid, Andreelli, Fabrizio, Barthelemy, Olivier, Bastard, Jean-Philippe, Batisse, Jean-Paul, Berland, Magalie, Bittar, Randa, Blüher, Matthias, Bork, Peer, Bourron, Olivier, Camus, Mickael, Cassuto, Dominique, Ciangura, Cecile, Coelho, Luis Pedro, Collet, Jean-Philippe, Dumas, Marc-Emmanuel, Ehrlich, S. Dusko, Engelbrechtsen, Line, Fezeu, Leopold, Forslund, Sofia, Fromentin, Sebastien, Galan, Pilar, Giral, Philippe, Gøtze, Jens Peter, Hansen, Torben, Hansen, Tue H., Hartemann, Agnes, Hartmann, Bolette, Hercberg, Serge, Holmes, Bridget, Holst, Jens Juul, Hornbak, Malene, Hoyles, Lesley, Hulot, Jean-Sebastien, Jaqueminet, Sophie, Kerneis, Mathieu, Khemis, Jean, Kozlowski, Ruby, Pedersen, Helle Krogh, Kuhn, Michael, Mannerås-Holm, Louise, Marko, Lajos, Martinez-Gili Robin Massey, Laura, Maziers, Nicolas, Medina-Stamminger, Jonathan, Moitinho-Silva, Lucas, Montalescot, Gilles, Moutel, Sandrine, Neves, Ana Luisa, Olanipekun, Michael, Oppert, Jean-Michel, Poitou, Christine, Pousset, Francoise, Pouzoulet, Laurence, Rouault, Christine, Silvain, Johanne, and Vestergaard, Henrik

Abstract

Over the past years, it has become clear that the microbial ecosystem in the gut has a profound capacity to interact with the host through the production of a wide range of bioactive metabolites. The microbially produced metabolite imidazole propionate (ImP) is clinically and mechanistically linked with insulin resistance and type 2 diabetes, but it is unclear how ImP is associated with heart failure.

Published
2023
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17. 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, primary, Brorsson, Caroline A., additional, Bizzotto, Roberto, additional, Mari, Andrea, additional, Tura, Andrea, additional, Koivula, Robert, additional, Mahajan, Anubha, additional, Vinuela, Ana, additional, Tajes, Juan Fernandez, additional, Sharma, Sapna, additional, Haid, Mark, additional, Prehn, Cornelia, additional, Artati, Anna, additional, Hong, Mun-Gwan, additional, Musholt, Petra B., additional, Kurbasic, Azra, additional, De Masi, Federico, additional, Tsirigos, Kostas, additional, Pedersen, Helle Krogh, additional, Gudmundsdottir, Valborg, additional, Thomas, Cecilia Engel, additional, Banasik, Karina, additional, Jennison, Chrisopher, additional, Jones, Angus, additional, Kennedy, Gwen, additional, Bell, Jimmy, additional, Thomas, Louise, additional, Frost, Gary, additional, Thomsen, Henrik, additional, Allin, Kristine, additional, Hansen, Tue Haldor, additional, Vestergaard, Henrik, additional, Hansen, Torben, additional, Rutters, Femke, additional, Elders, Petra, additional, t’Hart, Leen, additional, Bonnefond, Amelie, additional, Canouil, Mickaël, additional, Brage, Soren, additional, Kokkola, Tarja, additional, Heggie, Alison, additional, McEvoy, Donna, additional, Hattersley, Andrew, additional, McDonald, Timothy, additional, Teare, Harriet, additional, Ridderstrale, Martin, additional, Walker, Mark, additional, Forgie, Ian, additional, Giordano, Giuseppe N., additional, Froguel, Philippe, additional, Pavo, Imre, additional, Ruetten, Hartmut, additional, Pedersen, Oluf, additional, Dermitzakis, Emmanouil, additional, Franks, Paul W., additional, Schwenk, Jochen M., additional, Adamski, Jerzy, additional, Pearson, Ewan, additional, McCarthy, Mark I., additional, and Brunak, Søren, additional

Published
2022
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18. Conjugated C-6 hydroxylated bile acids in serum relate to human metabolic health and gut Clostridia species

Author

Petersen, Anders Ø., Julienne, Hanna, Hyötyläinen, Tuulia, Sen, Partho, Fan, Yong, Pedersen, Helle Krogh, Jäntti, Sirkku, Hansen, Tue H., Nielsen, Trine, Jørgensen, Torben, Hansen, Torben, Myers, Pernille Neve, Nielsen, H. Bjørn, Ehrlich, S. Dusko, Orešič, Matej, Pedersen, Oluf, Petersen, Anders Ø., Julienne, Hanna, Hyötyläinen, Tuulia, Sen, Partho, Fan, Yong, Pedersen, Helle Krogh, Jäntti, Sirkku, Hansen, Tue H., Nielsen, Trine, Jørgensen, Torben, Hansen, Torben, Myers, Pernille Neve, Nielsen, H. Bjørn, Ehrlich, S. Dusko, Orešič, Matej, and Pedersen, Oluf

Abstract

Knowledge about in vivo effects of human circulating C-6 hydroxylated bile acids (BAs), also called muricholic acids, is sparse. It is unsettled if the gut microbiome might contribute to their biosynthesis. Here, we measured a range of serum BAs and related them to markers of human metabolic health and the gut microbiome. We examined 283 non-obese and obese Danish adults from the MetaHit study. Fasting concentrations of serum BAs were quantified using ultra-performance liquid chromatography-tandem mass-spectrometry. The gut microbiome was characterized with shotgun metagenomic sequencing and genome-scale metabolic modeling. We find that tauro- and glycohyocholic acid correlated inversely with body mass index (P = 4.1e-03, P = 1.9e-05, respectively), waist circumference (P = 0.017, P = 1.1e-04, respectively), body fat percentage (P = 2.5e-03, P = 2.3e-06, respectively), insulin resistance (P = 0.051, P = 4.6e-4, respectively), fasting concentrations of triglycerides (P = 0.06, P = 9.2e-4, respectively) and leptin (P = 0.067, P = 9.2e-4). Tauro- and glycohyocholic acids, and tauro-a-muricholic acid were directly linked with a distinct gut microbial community primarily composed of Clostridia species (P = 0.037, P = 0.013, P = 0.027, respectively). We conclude that serum conjugated C-6-hydroxylated BAs associate with measures of human metabolic health and gut communities of Clostridia species. The findings merit preclinical interventions and human feasibility studies to explore the therapeutic potential of these BAs in obesity and type 2 diabetes.

Published
2021

19. Conjugated C-6 hydroxylated bile acids in serum relate to human metabolic health and gut Clostridia species

Author

Petersen, Anders, Julienne, Hanna, Hyötyläinen, Tuulia, Sen, Partho, Fan, Yong, Pedersen, Helle Krogh, Jäntti, Sirkku, Hansen, Tue H., Nielsen, Trine, Jørgensen, Torben, Hansen, Torben, Myers, Pernille Neve, Nielsen, H. Bjørn, Ehrlich, S. Dusko, Orešič, Matej, Pedersen, Oluf, Petersen, Anders, Julienne, Hanna, Hyötyläinen, Tuulia, Sen, Partho, Fan, Yong, Pedersen, Helle Krogh, Jäntti, Sirkku, Hansen, Tue H., Nielsen, Trine, Jørgensen, Torben, Hansen, Torben, Myers, Pernille Neve, Nielsen, H. Bjørn, Ehrlich, S. Dusko, Orešič, Matej, and Pedersen, Oluf

Abstract

Knowledge about in vivo effects of human circulating C-6 hydroxylated bile acids (BAs), also called muricholic acids, is sparse. It is unsettled if the gut microbiome might contribute to their biosynthesis. Here, we measured a range of serum BAs and related them to markers of human metabolic health and the gut microbiome. We examined 283 non-obese and obese Danish adults from the MetaHit study. Fasting concentrations of serum BAs were quantified using ultra-performance liquid chromatography-tandem mass-spectrometry. The gut microbiome was characterized with shotgun metagenomic sequencing and genome-scale metabolic modeling. We find that tauro- and glycohyocholic acid correlated inversely with body mass index (P = 4.1e-03, P = 1.9e-05, respectively), waist circumference (P = 0.017, P = 1.1e-04, respectively), body fat percentage (P = 2.5e-03, P = 2.3e-06, respectively), insulin resistance (P = 0.051, P = 4.6e-4, respectively), fasting concentrations of triglycerides (P = 0.06, P = 9.2e-4, respectively) and leptin (P = 0.067, P = 9.2e-4). Tauro- and glycohyocholic acids, and tauro-a-muricholic acid were directly linked with a distinct gut microbial community primarily composed of Clostridia species (P = 0.037, P = 0.013, P = 0.027, respectively). We conclude that serum conjugated C-6-hydroxylated BAs associate with measures of human metabolic health and gut communities of Clostridia species. The findings merit preclinical interventions and human feasibility studies to explore the therapeutic potential of these BAs in obesity and type 2 diabetes.

Published
2021

20. Imidazole propionate is increased in diabetes and associated with dietary patterns and altered microbial ecology

Author

Bel Lassen, Pierre, Nielsen, Trine, Bergh, Per-Olof, Rouault, Christine, André, Sébastien, Marquet, Florian, Andreelli, Fabrizio, Salem, Joe-Elie, Assmann, Karen, Bastard, Jean-Philippe, Forslund, Sofia, Le Chatelier, Emmanuelle, Falony, Gwen, Pons, Nicolas, Prifti, Edi, Quinquis, Benoit, Roume, Hugo, Vieira-Silva, Sara, Hansen, Tue, Pedersen, Helle Krogh, Lewinter, Christian, Sønderskov, Nadja, Vestergaard, Henrik, Raes, Jeroen, Nielsen, Jens, Bork, Peer, Ehrlich, S. Dusko, Pedersen, Oluf, Aron-Wisneswky, Judith, Clément, Karine, Bäckhed, Fredrik, Molinaro, Antonio, Lassen, Pierre, Henricsson, Marcus, Wu, Hao, Adriouch, Solia, Belda, Eugeni, Chakaroun, Rima, Nielse, Trine, Bergh, Christine, Rouault, Sébastien, Andr, Florian, Marquet, Fabrizio, Andreelli, Joe-Elie, Salem, Karen, Assmann, Jean-Philippe, Bastard, Sofia, Forslund, Emmanuelle, Le Chatelier, Gwen, Falon, Nicolas, Pons, Edi, Prift, Benoit, Quinquis, Hugo, Roume, Sara, Vieira-Silv, Tue, Hansen, Krogh, Pedersen, Christian, Lewinter, Nadja, The, Metacardis, Køber, Lars, Vestergaar, Henrik, Hansen, Torben, Zucker, Jean-Daniel, Galan, Pilar, Dumas, Marc-Emmanuel, Rae, Jeroen, Oppert, Jean-Michel, Letunic, Ivica, Nielse, Jens, Ehrlic, S, Stumvoll, Michael, Pederse, Oluf, Aron-Wisnewsky, Judith, Bäckhe, Fredrik, Sahlgrenska Center for Cardiovascular and Metabolic Research, Partenaires INRAE, Sahlgrenska University Hospital, Nutrition et obésités: approches systémiques (nutriomics) (UMR-S 1269 INSERM - Sorbonne Université), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU), Centre de Recherche en Nutrition Humaine d'Ile-de-France (CRNH-IDF), Institut de Veille Sanitaire (INVS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Institut National Agronomique Paris-Grignon (INA P-G)-CETAF-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Sorbonne Paris Nord, Unité de Recherche sur les Maladies Cardiovasculaires, du Métabolisme et de la Nutrition = Institute of cardiometabolism and nutrition (ICAN), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université-Sorbonne Université (SU), Medical Department III – Endocrinology, Nephrology, Rheumatology, Universität Leipzig [Leipzig], Novo Nordisk Foundation Center for Basic Metabolic Research (CBMR), Faculty of Health and Medical Sciences, University of Copenhagen = Københavns Universitet (KU)-University of Copenhagen = Københavns Universitet (KU), Centre d'Investigation Clinique de Paris Est, Institut National de la Santé et de la Recherche Médicale (INSERM), AP-HP Hôpital Tenon [Paris], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Experimental and Clinical Research Center [Berlin, Germany], Max Delbrück Center for Molecular Medicine [Berlin] (MDC), Helmholtz-Gemeinschaft = Helmholtz Association-Helmholtz-Gemeinschaft = Helmholtz Association-Charité - Universitätsmedizin Berlin / Charite - University Medicine Berlin, MICrobiologie de l'ALImentation au Service de la Santé (MICALIS), AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Department of Microbiology and Immunology, Rega Institute for Medical Research, Laboratory of Molecular Bacteriology, Université Catholique de Louvain (UCL), VIB-KU Leuven Center for Microbiology [Belgium], Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven), Unité de modélisation mathématique et informatique des systèmes complexes [Bondy] (UMMISCO), Sorbonne Université (SU)-Universtié Yaoundé 1 [Cameroun]-Université Cadi Ayyad [Marrakech] (UCA)-Université Gaston Bergé (Saint-Louis, Sénégal)-Université Cheikh Anta Diop [Dakar, Sénégal] (UCAD)-Institut de la francophonie pour l'informatique-Institut de Recherche pour le Développement (IRD [France-Nord]), Equipe 3: EREN- Equipe de Recherche en Epidémiologie Nutritionnelle (CRESS - U1153), Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS (U1153 / UMR_A_1125 / UMR_S_1153)), Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Department of Metabolism, Digestion and Reproduction, Imperial College London, National Heart and Lung Institute [London] (NHLI), Royal Brompton and Harefield NHS Foundation Trust-Imperial College London, Centre de Recherche en Nutrition Humaine - Ile de France (CRNH - IDF), Biobyte Solutions GMBH, Nutrition et obésités: approches systémiques (UMR-S 1269) (Nutriomics), Institut de Veille Sanitaire (INVS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Institut National Agronomique Paris-Grignon (INA P-G)-CETAF-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Sorbonne Paris Nord, University of Copenhagen = Københavns Universitet (UCPH)-University of Copenhagen = Københavns Universitet (UCPH), Sahlgrenska Academy at University of Gothenburg [Göteborg], CHU Tenon [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Helmholtz-Gemeinschaft = Helmholtz Association-Helmholtz-Gemeinschaft = Helmholtz Association-Charité - UniversitätsMedizin = Charité - University Hospital [Berlin], Helmholtz-Gemeinschaft = Helmholtz Association, Charité - UniversitätsMedizin = Charité - University Hospital [Berlin], Rega Institute for Medical Research [Leuven, België], Unité de Recherche sur les Maladies Cardiovasculaires, du Métabolisme et de la Nutrition = Research Unit on Cardiovascular and Metabolic Diseases (ICAN), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Institut de Cardiométabolisme et Nutrition = Institute of Cardiometabolism and Nutrition [CHU Pitié Salpêtrière] (IHU ICAN), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-CHU Pitié-Salpêtrière [AP-HP], Université de Yaoundé I-Institut de la francophonie pour l'informatique-Université Cheikh Anta Diop [Dakar, Sénégal] (UCAD)-Université Gaston Bergé (Saint-Louis, Sénégal)-Université Cadi Ayyad [Marrakech] (UCA)-Sorbonne Université (SU)-Institut de Recherche pour le Développement (IRD [France-Nord]), Chalmers University of Technology [Göteborg], European Molecular Biology Laboratory [Heidelberg] (EMBL), Université Sorbonne Paris Nord-Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS (U1153 / UMR_A_1125 / UMR_S_1153)), Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Imperial College London-Royal Brompton and Harefield NHS Foundation Trust, HAL-SU, Gestionnaire, Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Institut de Veille Sanitaire (INVS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Université Sorbonne Paris Nord-Institut National Agronomique Paris-Grignon (INA P-G)-Sorbonne Université (SU)-CETAF-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National de la Santé et de la Recherche Médicale (INSERM), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU), Institut de Recherche pour le Développement (IRD [France-Nord])-Institut de la francophonie pour l'informatique-Université Cheikh Anta Diop [Dakar, Sénégal] (UCAD)-Université Gaston Bergé (Saint-Louis, Sénégal)-Université Cadi Ayyad [Marrakech] (UCA)-Université de Yaoundé I-Sorbonne Université (SU), Charité - Universitätsmedizin Berlin / Charite - University Medicine Berlin, Auteur indépendant, Commission of the European Communities, and Universität Leipzig

Subjects
0301 basic medicine, Male, ACCURATE METHOD, endocrine system diseases, Metabolite, [SDV]Life Sciences [q-bio], General Physics and Astronomy, Type 2 diabetes, GUT MICROBIOME, GLUCOSE, Cohort Studies, chemistry.chemical_compound, 0302 clinical medicine, Endocrinology, [CHIM] Chemical Sciences, Prediabetes, lcsh:Science, 2. Zero hunger, RISK, Multidisciplinary, biology, INSULIN SENSITIVITY, Imidazoles, HOMEOSTASIS MODEL ASSESSMENT, Middle Aged, 3. Good health, Multidisciplinary Sciences, [SDV] Life Sciences [q-bio], 030220 oncology & carcinogenesis, Science & Technology - Other Topics, Enterotype, Female, Adult, medicine.medical_specialty, Science, Carbohydrate metabolism, Microbiology, digestive system, Article, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, INTESTINAL MICROBIOTA, Internal medicine, medicine, Humans, [CHIM]Chemical Sciences, Histidine, Author Correction, PHYSIOLOGY, Aged, Science & Technology, Bacteria, General Chemistry, Metabolism, medicine.disease, biology.organism_classification, Gastrointestinal Microbiome, MetaCardis Consortium, METAGENOME, INDIVIDUALS, 030104 developmental biology, chemistry, Diabetes Mellitus, Type 2, Cardiovascular and Metabolic Diseases, lcsh:Q, Bacteroides
Abstract

Microbiota-host-diet interactions contribute to the development of metabolic diseases. Imidazole propionate is a novel microbially produced metabolite from histidine, which impairs glucose metabolism. Here, we show that subjects with prediabetes and diabetes in the MetaCardis cohort from three European countries have elevated serum imidazole propionate levels. Furthermore, imidazole propionate levels were increased in subjects with low bacterial gene richness and Bacteroides 2 enterotype, which have previously been associated with obesity. The Bacteroides 2 enterotype was also associated with increased abundance of the genes involved in imidazole propionate biosynthesis from dietary histidine. Since patients and controls did not differ in their histidine dietary intake, the elevated levels of imidazole propionate in type 2 diabetes likely reflects altered microbial metabolism of histidine, rather than histidine intake per se. Thus the microbiota may contribute to type 2 diabetes by generating imidazole propionate that can modulate host inflammation and metabolism., Gut microbial metabolism of nutrients contributes to metabolic diseases, and the histidine metabolite imidazole propionate (ImP) is produced by type 2 diabetes (T2D) associated microbiome. Here the authors report that circulating ImP levels are increased in subjects with prediabetes or T2D in three European populations, and this increase associates with altered gut microbiota rather than dietary histidine.

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

Author

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

Abstract

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and, ultimately, hepatocellular carcinomas. We sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning.METHODS AND FINDINGS: We utilized the baseline data from IMI DIRECT, a multicenter prospective cohort study of 3,029 European-ancestry adults recently diagnosed with T2D (n = 795) or at high risk of developing the disease (n = 2,234). Multi-omics (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI-image-derived liver fat content (CONCLUSIONS: In this study, we developed several models with different combinations of clinical and omics data and identified biological features that appear to be associated with liver fat accumulation. In general, the clinical variables showed better prediction ability than the complex omics variables. However, the combination of omics and clinical variables yielded the highest accuracy. We have incorporated the developed clinical models into a web interface (see: https://www.predictliverfat.org/) and made it available to the community.TRIAL REGISTRATION: ClinicalTrials.gov NCT03814915.

Published
2020
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22. Additional file 1 of 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, Masi, Federico De, Elders, Petra J., Forgie, Ian, Giordano, Giuseppe N., Grallert, Harald, Ramneek Gupta, Haid, Mark, Hansen, Torben, Hansen, Tue H., Hattersley, Andrew T., Heggie, Alison, Mun-Gwan Hong, Jones, Angus G., Koivula, Robert, Kokkola, Tarja, Laakso, Markku, Løngreen, Peter, Anubha Mahajan, 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., Jagadish Vangipurapu, 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

Abstract

Additional file 1. Supplementary Figures. This file contains Fig. S1 – S13.

Published
2020
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23. Additional file 2 of 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, Masi, Federico De, Elders, Petra J., Forgie, Ian, Giordano, Giuseppe N., Grallert, Harald, Ramneek Gupta, Haid, Mark, Hansen, Torben, Hansen, Tue H., Hattersley, Andrew T., Heggie, Alison, Mun-Gwan Hong, Jones, Angus G., Koivula, Robert, Kokkola, Tarja, Laakso, Markku, Løngreen, Peter, Anubha Mahajan, 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., Jagadish Vangipurapu, 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

Subjects
Data_FILES
Abstract

Additional file 2. Supplementary Methods. This file contains methods descriptions for omics data generation and preprocessing.

Published
2020
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24. 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, Brunak, Søren, 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

Abstract

Background: The rising prevalence of type 2 diabetes (T2D) poses a major global challenge. It remains unresolved to what extent transcriptomic signatures of metabolic dysregulation and T2D can be observed in easily accessible tissues such as blood. Additionally, large-scale human studies are required to further our understanding of the putative inflammatory component of insulin resistance and T2D. Here we used transcriptomics data from individuals with (n = 789) and without (n = 2127) T2D from the IMI-DIRECT cohorts to describe the co-expression structure of whole blood that mainly reflects processes and cell types of the immune system, and how it relates to metabolically relevant clinical traits and T2D. Methods: Clusters of co-expressed genes were identified in the non-diabetic IMI-DIRECT cohort and evaluated with regard to stability, as well as preservation and rewiring in the cohort of individuals with T2D. We performed functional and immune cell signature enrichment analyses, and a genome-wide association study to describe the genetic regulation of the modules. Phenotypic and trans-omics associations of the transcriptomic modules were investigated across both IMI-DIRECT cohorts. Results: We identified 55 whole blood co-expression modules, some of which clustered in larger super-modules. We identified a large number of associations between these transcriptomic modules and measures of insulin action and glucose tolerance. Some of the metabolically linked modules reflect neutrophil-lymphocyte ratio in blood while others are independent of white blood cell estimates, including a module of genes encoding neutrophil granule proteins with antibacterial properties for which the strongest associations with clinical traits and T2D status were observed. Through the integration of genetic and multi-omics data, we provide a holistic view of the regulation and molecular context of whole blood transcriptomic modules. We furthermore identified an overlap between genetic sig

Published
2020

25. Imidazole propionate is increased in diabetes and associated with dietary patterns and altered microbial ecology

Author

Molinaro, Antonio, Bel Lassen, Pierre, Henricsson, Marcus, Wu, Hao, Adriouch, Solia, Belda, Eugeni, Chakaroun, Rima, Nielsen, Trine, Bergh, Per-Olof, Rouault, Christine, Andre, Sebastien, Marquet, Florian, Andreelli, Fabrizio, Salem, Joe-Elie, Assmann, Karen, Bastard, Jean-Philippe, Forslund, Sofia, Le Chatelier, Emmanuelle, Falony, Gwen, Pons, Nicolas, Prifti, Edi, Quinquis, Benoit, Roume, Hugo, Vieira-Silva, Sara, Hansen, Tue H., Pedersen, Helle Krogh, Lewinter, Christian, Sønderskov, Nadja B., Køber, Lars, Vestergaard, Henrik, Hansen, Torben, Zucker, Jean-Daniel, Galan, Pilar, Dumas, Marc-Emmanuel, Raes, Jeroen, Oppert, Jean-Michel, Letunic, Ivica, Nielsen, Jens, Bork, Peer, Ehrlich, S. Dusko, Stumvoll, Michael, Pedersen, Oluf, Aron-Wisneswky, Judith, Clement, Karine, Baeckhed, Fredrik, Molinaro, Antonio, Bel Lassen, Pierre, Henricsson, Marcus, Wu, Hao, Adriouch, Solia, Belda, Eugeni, Chakaroun, Rima, Nielsen, Trine, Bergh, Per-Olof, Rouault, Christine, Andre, Sebastien, Marquet, Florian, Andreelli, Fabrizio, Salem, Joe-Elie, Assmann, Karen, Bastard, Jean-Philippe, Forslund, Sofia, Le Chatelier, Emmanuelle, Falony, Gwen, Pons, Nicolas, Prifti, Edi, Quinquis, Benoit, Roume, Hugo, Vieira-Silva, Sara, Hansen, Tue H., Pedersen, Helle Krogh, Lewinter, Christian, Sønderskov, Nadja B., Køber, Lars, Vestergaard, Henrik, Hansen, Torben, Zucker, Jean-Daniel, Galan, Pilar, Dumas, Marc-Emmanuel, Raes, Jeroen, Oppert, Jean-Michel, Letunic, Ivica, Nielsen, Jens, Bork, Peer, Ehrlich, S. Dusko, Stumvoll, Michael, Pedersen, Oluf, Aron-Wisneswky, Judith, Clement, Karine, and Baeckhed, Fredrik

Abstract

Microbiota-host-diet interactions contribute to the development of metabolic diseases. Imidazole propionate is a novel microbially produced metabolite from histidine, which impairs glucose metabolism. Here, we show that subjects with prediabetes and diabetes in the MetaCardis cohort from three European countries have elevated serum imidazole propionate levels. Furthermore, imidazole propionate levels were increased in subjects with low bacterial gene richness and Bacteroides 2 enterotype, which have previously been associated with obesity. The Bacteroides 2 enterotype was also associated with increased abundance of the genes involved in imidazole propionate biosynthesis from dietary histidine. Since patients and controls did not differ in their histidine dietary intake, the elevated levels of imidazole propionate in type 2 diabetes likely reflects altered microbial metabolism of histidine, rather than histidine intake per se. Thus the microbiota may contribute to type 2 diabetes by generating imidazole propionate that can modulate host inflammation and metabolism. Gut microbial metabolism of nutrients contributes to metabolic diseases, and the histidine metabolite imidazole propionate (ImP) is produced by type 2 diabetes (T2D) associated microbiome. Here the authors report that circulating ImP levels are increased in subjects with prediabetes or T2D in three European populations, and this increase associates with altered gut microbiota rather than dietary histidine.

Published
2020

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

Author

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

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

Author

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

Published
2020
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28. Integrative network analysis highlights biological processes underlying GLP-1 stimulated insulin secretion:A DIRECT study

Author

Gudmundsdottir, Valborg, Pedersen, Helle Krogh, Allebrandt, Karla Viviani, Brorsson, Caroline Anna, van Leeuwen, Nienke, Banasik, Karina, Mahajan, Anubha, Groves, Christopher J, van de Bunt, Martijn, Dawed, Adem Y, Fritsche, Andreas, Staiger, Harald, Simonis-Bik, Annemarie M C, Deelen, Joris, Kramer, Mark H H, Dietrich, Axel, Hübschle, Thomas, Willemsen, Gonneke, Häring, Hans-Ulrich, de Geus, Eco J C, Boomsma, Dorret I, Eekhoff, Elisabeth M W, Ferrer, Jorge, McCarthy, Mark I, Pearson, Ewan R, Gupta, Ramneek, Brunak, Søren, 't Hart, Leen M, Gudmundsdottir, Valborg, Pedersen, Helle Krogh, Allebrandt, Karla Viviani, Brorsson, Caroline Anna, van Leeuwen, Nienke, Banasik, Karina, Mahajan, Anubha, Groves, Christopher J, van de Bunt, Martijn, Dawed, Adem Y, Fritsche, Andreas, Staiger, Harald, Simonis-Bik, Annemarie M C, Deelen, Joris, Kramer, Mark H H, Dietrich, Axel, Hübschle, Thomas, Willemsen, Gonneke, Häring, Hans-Ulrich, de Geus, Eco J C, Boomsma, Dorret I, Eekhoff, Elisabeth M W, Ferrer, Jorge, McCarthy, Mark I, Pearson, Ewan R, Gupta, Ramneek, Brunak, Søren, and 't Hart, Leen M

Abstract

Glucagon-like peptide 1 (GLP-1) stimulated insulin secretion has a considerable heritable component as estimated from twin studies, yet few genetic variants influencing this phenotype have been identified. We performed the first genome-wide association study (GWAS) of GLP-1 stimulated insulin secretion in non-diabetic individuals from the Netherlands Twin register (n = 126). This GWAS was enhanced using a tissue-specific protein-protein interaction network approach. We identified a beta-cell protein-protein interaction module that was significantly enriched for low gene scores based on the GWAS P-values and found support at the network level in an independent cohort from Tübingen, Germany (n = 100). Additionally, a polygenic risk score based on SNPs prioritized from the network was associated (P < 0.05) with glucose-stimulated insulin secretion phenotypes in up to 5,318 individuals in MAGIC cohorts. The network contains both known and novel genes in the context of insulin secretion and is enriched for members of the focal adhesion, extracellular-matrix receptor interaction, actin cytoskeleton regulation, Rap1 and PI3K-Akt signaling pathways. Adipose tissue is, like the beta-cell, one of the target tissues of GLP-1 and we thus hypothesized that similar networks might be functional in both tissues. In order to verify peripheral effects of GLP-1 stimulation, we compared the transcriptome profiling of ob/ob mice treated with liraglutide, a clinically used GLP-1 receptor agonist, versus baseline controls. Some of the upstream regulators of differentially expressed genes in the white adipose tissue of ob/ob mice were also detected in the human beta-cell network of genes associated with GLP-1 stimulated insulin secretion. The findings provide biological insight into the mechanisms through which the effects of GLP-1 may be modulated and highlight a potential role of the beta-cell expressed genes RYR2, GDI2, KIAA0232, COL4A1 and COL4A2 in GLP-1 stimulated insulin secre

Published
2018

29. Metabolite ratios as potential biomarkers for type 2 diabetes: a DIRECT study

Author

Molnos, Sophie, Wahl, Simone, Haid, Mark, Eekhoff, E. Marelise W., Pool, René, Floegel, Anna, Deelen, Joris, Much, Daniela, Prehn, Cornelia, Breier, Michaela, Draisma, Harmen H., van Leeuwen, Nienke, Simonis-Bik, Annemarie M.C., Jonsson, Anna Elisabet, Willemsen, Gonneke, Bernigau, Wolfgang, Wang-Sattler, Rui, Suhre, Karsten, Peters, Annette, Thorand, Barbara, Herder, Christian, Rathmann, Wolfgang, Roden, Michael, Gieger, Christian, Kramer, Mark H.H., van Heemst, Diana, Pedersen, Helle Krogh, Gudmundsdottir, Valborg, Schulze, Matthias B., Pischon, Tobias, de Geus, Eco J.C., Boeing, Heiner, Boomsma, Dorret I., Ziegler, Anette G., Slagboom, P. Eline, Hummel, Sandra, Beekman, Marian, Grallert, Harald, Brunak, Søren, McCarthy, Mark I., Gupta, Ramneek, Pearson, Ewan R., Adamski, Jerzy, 'T Hart, Leen M., Molnos, Sophie, Wahl, Simone, Haid, Mark, Eekhoff, E. Marelise W., Pool, René, Floegel, Anna, Deelen, Joris, Much, Daniela, Prehn, Cornelia, Breier, Michaela, Draisma, Harmen H., van Leeuwen, Nienke, Simonis-Bik, Annemarie M.C., Jonsson, Anna Elisabet, Willemsen, Gonneke, Bernigau, Wolfgang, Wang-Sattler, Rui, Suhre, Karsten, Peters, Annette, Thorand, Barbara, Herder, Christian, Rathmann, Wolfgang, Roden, Michael, Gieger, Christian, Kramer, Mark H.H., van Heemst, Diana, Pedersen, Helle Krogh, Gudmundsdottir, Valborg, Schulze, Matthias B., Pischon, Tobias, de Geus, Eco J.C., Boeing, Heiner, Boomsma, Dorret I., Ziegler, Anette G., Slagboom, P. Eline, Hummel, Sandra, Beekman, Marian, Grallert, Harald, Brunak, Søren, McCarthy, Mark I., Gupta, Ramneek, Pearson, Ewan R., Adamski, Jerzy, and 'T Hart, Leen M.

Abstract

Aims/hypothesis: Circulating metabolites have been shown to reflect metabolic changes during the development of type 2 diabetes. In this study we examined the association of metabolite levels and pairwise metabolite ratios with insulin responses after glucose, glucagon-like peptide-1 (GLP-1) and arginine stimulation. We then investigated if the identified metabolite ratios were associated with measures of OGTT-derived beta cell function and with prevalent and incident type 2 diabetes. Methods: We measured the levels of 188 metabolites in plasma samples from 130 healthy members of twin families (from the Netherlands Twin Register) at five time points during a modified 3 h hyperglycaemic clamp with glucose, GLP-1 and arginine stimulation. We validated our results in cohorts with OGTT data (n = 340) and epidemiological case–control studies of prevalent (n = 4925) and incident (n = 4277) diabetes. The data were analysed using regression models with adjustment for potential confounders. Results: There were dynamic changes in metabolite levels in response to the different secretagogues. Furthermore, several fasting pairwise metabolite ratios were associated with one or multiple clamp-derived measures of insulin secretion (all p <9.2 × 10−7). These associations were significantly stronger compared with the individual metabolite components. One of the ratios, valine to phosphatidylcholine acyl-alkyl C32:2 (PC ae C32:2), in addition showed a directionally consistent positive association with OGTT-derived measures of insulin secretion and resistance (p ≤ 5.4 × 10−3) and prevalent type 2 diabetes (ORVal_PC ae C32:2 2.64 [β 0.97 ± 0.09], p = 1.0 × 10−27). Furthermore, Val_PC ae C32:2 predicted incident diabetes independent of established risk factors in two epidemiological cohort studies (HRVal_PC ae C32:2 1.57 [β 0.45 ± 0.06]; p = 1.3 × 10−15), leading to modest improvements in the receiver operating characteristics when added to a model containing a set

Published
2018

30. Integrative network analysis highlights biological processes underlying GLP-1 stimulated insulin secretion: A DIRECT study

Author

Gudmundsdottir, Valborg, Pedersen, Helle Krogh, Allebrandt, Karla Viviani, Brorsson, Caroline Anna, van Leeuwen, Nienke, Banasik, Karina, Mahajan, Anubha, Groves, Christopher J, van de Bunt, Martijn, Dawed, Adem Y, Fritsche, Andreas, Staiger, Harald, Simonis-Bik, Annemarie M C, Deelen, Joris, Kramer, Mark H H, Dietrich, Axel, Hübschle, Thomas, Willemsen, Gonneke, Häring, Hans-Ulrich, de Geus, Eco J C, Boomsma, Dorret I, Eekhoff, Elisabeth M W, Ferrer, Jorge, McCarthy, Mark I, Pearson, Ewan R, Gupta, Ramneek, Brunak, Søren, 't Hart, Leen M, Gudmundsdottir, Valborg, Pedersen, Helle Krogh, Allebrandt, Karla Viviani, Brorsson, Caroline Anna, van Leeuwen, Nienke, Banasik, Karina, Mahajan, Anubha, Groves, Christopher J, van de Bunt, Martijn, Dawed, Adem Y, Fritsche, Andreas, Staiger, Harald, Simonis-Bik, Annemarie M C, Deelen, Joris, Kramer, Mark H H, Dietrich, Axel, Hübschle, Thomas, Willemsen, Gonneke, Häring, Hans-Ulrich, de Geus, Eco J C, Boomsma, Dorret I, Eekhoff, Elisabeth M W, Ferrer, Jorge, McCarthy, Mark I, Pearson, Ewan R, Gupta, Ramneek, Brunak, Søren, and 't Hart, Leen M

Abstract

Glucagon-like peptide 1 (GLP-1) stimulated insulin secretion has a considerable heritable component as estimated from twin studies, yet few genetic variants influencing this phenotype have been identified. We performed the first genome-wide association study (GWAS) of GLP-1 stimulated insulin secretion in non-diabetic individuals from the Netherlands Twin register (n = 126). This GWAS was enhanced using a tissue-specific protein-protein interaction network approach. We identified a beta-cell protein-protein interaction module that was significantly enriched for low gene scores based on the GWAS P-values and found support at the network level in an independent cohort from Tübingen, Germany (n = 100). Additionally, a polygenic risk score based on SNPs prioritized from the network was associated (P <0.05) with glucose-stimulated insulin secretion phenotypes in up to 5,318 individuals in MAGIC cohorts. The network contains both known and novel genes in the context of insulin secretion and is enriched for members of the focal adhesion, extracellular-matrix receptor interaction, actin cytoskeleton regulation, Rap1 and PI3K-Akt signaling pathways. Adipose tissue is, like the beta-cell, one of the target tissues of GLP-1 and we thus hypothesized that similar networks might be functional in both tissues. In order to verify peripheral effects of GLP-1 stimulation, we compared the transcriptome profiling of ob/ob mice treated with liraglutide, a clinically used GLP-1 receptor agonist, versus baseline controls. Some of the upstream regulators of differentially expressed genes in the white adipose tissue of ob/ob mice were also detected in the human beta-cell network of genes associated with GLP-1 stimulated insulin secretion. The findings provide biological insight into the mechanisms through which the effects of GLP-1 may be modulated and highlight a potential role of the beta-cell expressed genes RYR2, GDI2, KIAA0232, COL4A1 and COL4A2 in GLP-1 stimulated insulin secretion

Published
2018

31. Corrigendum:Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota

Author

Forslund, Kristoffer, Hildebrand, Falk, Nielsen, Trine, Falony, Gwen, Le Chatelier, Emmanuelle, Sunagawa, Shinichi, Prifti, Edi, Vieira-Silva, Sara, Gudmundsdottir, Valborg, Pedersen, Helle Krogh, Arumugam, Manimozhiyan, Kristiansen, Karsten, Voigt, Anita Yvonne, Vestergaard, Henrik, Hercog, Rajna, Costea, Paul Igor, Kultima, Jens Roat, Li, Junhua, Jørgensen, Torben, Levenez, Florence, Doré, Joël, Nielsen, Henrik Bjørn, Brunak, Søren, Raes, Jeroen, Hansen, Torben, Wang, Jun, Ehrlich, S. Dusko, Bork, Peer, and Pedersen, Oluf

Subjects
0301 basic medicine, Multidisciplinary, Serum insulin, Type 2 diabetes, Biology, medicine.disease, Bioinformatics, Affect (psychology), Phenotype, 03 medical and health sciences, 030104 developmental biology, Human gut, medicine, Journal Article, Metformin treatment, Glycated haemoglobin
Abstract

Nature 528, 262–266 (2015); doi:10.1038/nature15766 In the Supplementary Information to this Letter, data from two previous studies were used in the meta-analysis. However, the unit conversions used to make the data comparable were inconsistent for two of the included phenotype measures. Although this error does not affect the data used to generate the conclusions of the Letter, it might affect follow-up studies using the glycated haemoglobin (HbA1c) and serum insulin phenotypes.

Published
2017
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32. Integrative network analysis highlights biological processes underlying GLP-1 stimulated insulin secretion: A DIRECT study

Author

Gudmundsdottir, Valborg, primary, Pedersen, Helle Krogh, additional, Allebrandt, Karla Viviani, additional, Brorsson, Caroline, additional, van Leeuwen, Nienke, additional, Banasik, Karina, additional, Mahajan, Anubha, additional, Groves, Christopher J., additional, van de Bunt, Martijn, additional, Dawed, Adem Y., additional, Fritsche, Andreas, additional, Staiger, Harald, additional, Simonis-Bik, Annemarie M. C., additional, Deelen, Joris, additional, Kramer, Mark H. H., additional, Dietrich, Axel, additional, Hübschle, Thomas, additional, Willemsen, Gonneke, additional, Häring, Hans-Ulrich, additional, de Geus, Eco J. C., additional, Boomsma, Dorret I., additional, Eekhoff, Elisabeth M. W., additional, Ferrer, Jorge, additional, McCarthy, Mark I., additional, Pearson, Ewan R., additional, Gupta, Ramneek, additional, Brunak, Søren, additional, and ‘t Hart, Leen M., additional

Published
2018
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33. Pancreatic Islet Protein Complexes and Their Dysregulation in Type 2 Diabetes

Author

Pedersen, Helle Krogh, Gudmundsdottir, Valborg, Brunak, Søren, Pedersen, Helle Krogh, Gudmundsdottir, Valborg, and Brunak, Søren

Abstract

Type 2 diabetes (T2D) is a complex disease that involves multiple genes. Numerous risk loci have already been associated with T2D, although many susceptibility genes remain to be identified given heritability estimates. Systems biology approaches hold potential for discovering novel T2D genes by considering their biological context, such as tissue-specific protein interaction partners. Pancreatic islets are a key T2D tissue and many of the known genetic risk variants lead to impaired islet function, hence a better understanding of the islet-specific dysregulation in the disease-state is essential to unveil the full potential of person-specific profiles. Here we identify 3,692 overlapping pancreatic islet protein complexes (containing 10,805 genes) by integrating islet gene and protein expression data with protein interactions. We found 24 of these complexes to be significantly enriched for genes associated with diabetic phenotypes through heterogeneous evidence sources, including genetic variation, methylation, and gene expression in islets. The analysis specifically revealed ten T2D candidate genes with probable roles in islets (ANPEP, HADH, FAM105A, PDLIM4, PDLIM5, MAP2K4, PPP2R5E, SNX13, GNAS, and FRS2), of which the last six are novel in the context of T2D and the data that went into the analysis. Fifteen of the twenty-four complexes were further enriched for combined genetic associations with glycemic traits, exemplifying how perturbation of protein complexes by multiple small effects can give rise to diabetic phenotypes. The complex nature of T2D ultimately prompts an understanding of the individual patients at the network biology level. We present the foundation for such work by exposing a subset of the global interactome that is dysregulated in T2D and consequently provides a good starting point when evaluating an individual's alterations at the genome, transcriptome, or proteome level in relation to T2D in clinical settings.

Published
2017

36. Ranking factors involved in diabetes remission after bariatric surgery using machine-learning integrating clinical and genomic biomarkers

Author

Pedersen, Helle Krogh, Gudmundsdottir, Valborg, Pedersen, Mette Krogh, Brorsson, Caroline, Brunak, Søren, Gupta, Ramneek, Pedersen, Helle Krogh, Gudmundsdottir, Valborg, Pedersen, Mette Krogh, Brorsson, Caroline, Brunak, Søren, and Gupta, Ramneek

Abstract

As weight-loss surgery is an effective treatment for the glycaemic control of type 2 diabetes in obese patients, yet not all patients benefit, it is valuable to find predictive factors for this diabetic remission. This will help elucidating possible mechanistic insights and form the basis for prioritising obese patients with dysregulated diabetes for surgery where diabetes remission is of interest. In this study, we combine both clinical and genomic factors using heuristic methods, informed by prior biological knowledge in order to rank factors that would have a role in predicting diabetes remission, and indeed in identifying patients who may have low likelihood in responding to bariatric surgery for improved glycaemic control. Genetic variants from the Illumina CardioMetaboChip were prioritised through single-association tests and then seeded a larger selection from protein-protein interaction networks. Artificial neural networks allowing nonlinear correlations were trained to discriminate patients with and without surgery-induced diabetes remission, and the importance of each clinical and genetic parameter was evaluated. The approach highlighted insulin treatment, baseline HbA1c levels, use of insulin-sensitising agents and baseline serum insulin levels, as the most informative variables with a decent internal validation performance (74% accuracy, area under the curve (AUC) 0.81). Adding information for the eight top-ranked single nucleotide polymorphisms (SNPs) significantly boosted classification performance to 84% accuracy (AUC 0.92). The eight SNPs mapped to eight genes - ABCA1, ARHGEF12, CTNNBL1, GLI3, PROK2, RYBP, SMUG1 and STXBP5 - three of which are known to have a role in insulin secretion, insulin sensitivity or obesity, but have not been indicated for diabetes remission after bariatric surgery before.

Published
2016

37. Pharmacogenomics in diabetes mellitus:insights into drug action and drug discovery

Author

Zhou, Kaixin, Pedersen, Helle Krogh, Dawed, Adem Y., Pearson, Ewan R., Zhou, Kaixin, Pedersen, Helle Krogh, Dawed, Adem Y., and Pearson, Ewan R.

Abstract

Genomic studies have greatly advanced our understanding of the multifactorial aetiology of type 2 diabetes mellitus (T2DM) as well as the multiple subtypes of monogenic diabetes mellitus. In this Review, we discuss the existing pharmacogenetic evidence in both monogenic diabetes mellitus and T2DM. We highlight mechanistic insights from the study of adverse effects and the efficacy of antidiabetic drugs. The identification of extreme sulfonylurea sensitivity in patients with diabetes mellitus owing to heterozygous mutations in HNF1A represents a clear example of how pharmacogenetics can direct patient care. However, pharmacogenomic studies of response to antidiabetic drugs in T2DM has yet to be translated into clinical practice, although some moderate genetic effects have now been described that merit follow-up in trials in which patients are selected according to genotype. We also discuss how future pharmacogenomic findings could provide insights into treatment response in diabetes mellitus that, in addition to other areas of human genetics, facilitates drug discovery and drug development for T2DM.

Published
2016

41. Human gut microbes impact host serum metabolome and insulin sensitivity

Author

Pedersen, Helle Krogh, Gudmundsdottir, Valborg, Nielsen, Henrik Bjørn, Hyotylainen, Tuulia, Nielsen, Trine, Jensen, Benjamin A H, Forslund, Kristoffer, Hildebrand, Falk, Prifti, Edi, Falony, Gwen, Le Chatelier, Emmanuelle, Levenez, Florence, Dore, Joel, Mattila, Ismo, Plichta, Damian R, Pöhö, Päivi, Hellgren, Lars I, Arumugam, Manimozhiyan, Sunagawa, Shinichi, Vieira-Silva, Sara, Jørgensen, Torben, Holm, Jacob Bak, Trošt, Kajetan, Kristiansen, Karsten, Brix, Susanne, Raes, Jeroen, Wang, Jun, Hansen, Torben, Bork, Peer, Brunak, Søren, Oresic, Matej, Ehrlich, S. Dusko, Pedersen, Oluf, MetaHIT Consortium, ., Almeida, Mathieu, Batto, Jean-Michel, Blottiere, Hervé, Cultrone, Antonietta, Delorme, Christine, Dervyn, Rozenn, Guedon, Eric, Haimet, Florence, Jamet, Alexandre, Juste, Catherine, Kennedy, Sean, Kaci, Ghalia, Kleerebezem, Michiel, Layec, Séverine, Leclerc, Marion, Léonard, Pierre, Maguin, Emmanuelle, Manichanh, Chaysavanh, Pons, Nicolas, Renault, Pierre, Sanchez, Nicolas, Van De Guchte, Maarten, Van Hylckama Vlieg, Johan, Vandemeulebrouck, Gaetana, Winogradsky, Yohanan, Center for Biological Sequence Analysis - Department of Systems Biology, Danmarks Tekniske Universitet = Technical University of Denmark (DTU), University of Denmark, Turku Centre for Biotechnology, University of Turku-Åbo Academy University, Åbo Akademi University [Turku], Örebro University, VTT Technical Research Centre of Finland (VTT), Novo Nordisk Foundation Center for Basic Metabolic Research (CBMR), Faculty of Health and Medical Sciences, University of Copenhagen = Københavns Universitet (UCPH)-University of Copenhagen = Københavns Universitet (UCPH), Laboratory of Genomics and Molecular Biomedicine - Department of Biology, University of Copenhagen = Københavns Universitet (UCPH), European Molecular Biology Laboratory, Department of Bioscience Engineering, University of Antwerp (UA), Center for the Biology of Disease, VIB, MetaGenoPolis, Institut National de la Recherche Agronomique (INRA), Unité de Recherche sur les Maladies Cardiovasculaires, du Métabolisme et de la Nutrition = Research Unit on Cardiovascular and Metabolic Diseases (ICAN), Université Pierre et Marie Curie - Paris 6 (UPMC)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU), Centter for the Biology of Disease, Department of Microbiology and Immunology, Rega Institute for Medical Research, MICrobiologie de l'ALImentation au Service de la Santé (MICALIS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Steno Diabetes Center, Faculty of Pharmacy, University of Valencia, Institute of Microbiology, Université de Lausanne = University of Lausanne (UNIL), Centre for the Biology of Disease, Research Centre for Prevention and Health - Centre for Health, Glostrup Hospital, Beijing Genomics Institute [Shenzhen] (BGI), Vrije Universiteit Brussel [Bruxelles] (VUB), Princess Al Jawhara Albrahim Center of Excellence in the Research of Hereditary Disorders, King Abdulaziz University, Macau University of Science and Technology (MUST), Department of Medicine and State Key Laboratory of Pharmaceutical Biotechnology, University of Hong Kong, Faculty of Health Sciences, Aarhus University [Aarhus], Molecular Medicine Partnership Unit, Heidelberg University, Max Delbrück Center for Molecular Medicine [Berlin] (MDC), Helmholtz-Gemeinschaft = Helmholtz Association, Department of Bioinformatics, Erasmus University Medical Center [Rotterdam] (Erasmus MC), Disease Systems Biology [Copenhagen], Novo Nordisk Foundation Center for Protein Research (CPR), University of Copenhagen = Københavns Universitet (UCPH)-University of Copenhagen = Københavns Universitet (UCPH)-Faculty of Health and Medical Sciences, Centre for Biotechnology, Silesian University of Technology, Université Paris-Saclay, Centre for Host–Microbiome Interactions - Dental Institute Central Office, King‘s College London, Guys Hospital, Génie et Microbiologie des Procédés Alimentaires (GMPA), Département Microbiologie et Chaîne Alimentaire (MICA), Danone Research, Groupe Danone, International Human Microbiome Standards [FP7-HEALTH-2010-261376], Metagenopolis [ANR-11-DPBS-0001], Novo Nordisk Foundation, Lundbeck Foundation, European Project: 222720,EC | FP7 | SP1 | KBBE ,FP7-KBBE-2007-2A,TORNADO(2009), Technical University of Denmark [Lyngby] (DTU), University of Copenhagen = Københavns Universitet (KU)-University of Copenhagen = Københavns Universitet (KU), University of Copenhagen = Københavns Universitet (KU), Jouy-en-Josas, Unité de Recherche sur les Maladies Cardiovasculaires, du Métabolisme et de la Nutrition = Institute of cardiometabolism and nutrition (ICAN), Université Pierre et Marie Curie - Paris 6 (UPMC)-Assistance publique - Hôpitaux de Paris (AP-HP) (APHP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [APHP], Université de Lausanne (UNIL), BGI Shenzhen, Vrije University, University of Heidelberg, Max Delbrück Center for Molecular Medicine, University of Copenhagen = Københavns Universitet (KU)-University of Copenhagen = Københavns Universitet (KU)-Faculty of Health and Medical Sciences, US 1367 MGP MetaGénoPolis, Laboratory of Microbiology, CHU Rouen, Normandie Université (NU)-Normandie Université (NU), AgroParisTech-Institut National de la Recherche Agronomique (INRA), Digestive System Research Unit, Vall d'Hebron University Hospital [Barcelona], European Project: 201052, Faculty of Sciences and Bioengineering Sciences, Microbial Interactions, Department of Bio-engineering Sciences, and Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)

Subjects
0301 basic medicine, Male, [SDV]Life Sciences [q-bio], Prevotella, Gastrointestinal Microbiome/physiology, Bacteroides/physiology, Type 2 diabetes, Fasting/blood, SERUM, Mice, 0302 clinical medicine, insulin resistance, Bacteroides, Netherlands, Multidisciplinary, biology, Research Support, Non-U.S. Gov't, Gastrointestinal Microbiome, Prevotella/physiology, Fasting, 3. Good health, Cardiovascular diseases, Cardiovascular Diseases/metabolism, Metabolome, Amino Acids, Branched-Chain/biosynthesis, medicine.medical_specialty, Glyco-Forum Section, Serum/metabolism, mice, 030209 endocrinology & metabolism, metagenome, 03 medical and health sciences, Insulin resistance, Internal medicine, Glucose Intolerance, medicine, Animals, Humans, Microbiome, Glucose Intolerance/blood, ta1182, biology.organism_classification, medicine.disease, Obesity, Mice, Inbred C57BL, 030104 developmental biology, Endocrinology, Metagenome, Insulin Resistance, Amino Acids, Branched-Chain
Abstract

MetaHIT Consortium: Almeida M, Antolin M, Artiguenave F, Batto JM, Bertalan M, Blottiere H, Boruel N, Brechot C, Bruls T, Burgdorf K, Casellas F, Cultrone A, de Vos WM, Delorme C, Denariaraz G., Derrien M, Dervyn R, Feng Q, Grarup N, Guarner F, Guedon E, Haimet F, Jamet A, Juncker A, Juste C, Kennedy S, Khaci G, Kleerebezem M, Knoll J, Layec S, Leclerc M, Leonard P, LePaslier D, m'Rini C, Maguin E, Manichanh C, Mende D, Merieux A, Oozer R, Parkhill J, Pelletier E, POns N, QinJ, rasmussen S, Renault P, Rescigno M, Sanchez N, Sicheritz-Ponten T, Tap J, Tims S, Torrejon A, Turner K, van de Guchet M, van hylckama Vlieg JE, Vandemeulebrouck G, Varela E, Veiga P, Weissenbach J, Winogradski Y, Yamada T, Zoetendal EG; Insulin resistance is a forerunner state of ischaemic cardiovascular disease and type 2 diabetes. Here we show how the human gut microbiome impacts the serum metabolome and associates with insulin resistance in 277 non-diabetic Danish individuals. The serum metabolome of insulin-resistant individuals is characterized by increased levels of branched-chain amino acids (BCAAs), which correlate with a gut microbiome that has an enriched biosynthetic potential for BCAAs and is deprived of genes encoding bacterial inward transporters for these amino acids. Prevotella copri and Bacteroides vulgatus are identified as the main species driving the association between biosynthesis of BCAAs and insulin resistance, and in mice we demonstrate that P. copri can induce insulin resistance, aggravate glucose intolerance and augment circulating levels of BCAAs. Our findings suggest that microbial targets may have the potential to diminish insulin resistance and reduce the incidence of common metabolic and cardiovascular disorders.

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42. 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 (

43. 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 (

44. 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

45. Corrigendum: Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota.

Author

Forslund, Kristoffer, Hildebrand, Falk, Nielsen, Trine, Falony, Gwen, Le Chatelier, Emmanuelle, Sunagawa, Shinichi, Prifti, Edi, Vieira-Silva, Sara, Gudmundsdottir, Valborg, Pedersen, Helle Krogh, Arumugam, Manimozhiyan, Kristiansen, Karsten, Voigt, Anita Yvonne, Vestergaard, Henrik, Hercog, Rajna, Costea, Paul Igor, Kultima, Jens Roat, Li, Junhua, Jørgensen, Torben, and Levenez, Florence

Published
2017
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46. Infant Formula With a Specific Blend of Five Human Milk Oligosaccharides Drives the Gut Microbiota Development and Improves Gut Maturation Markers: A Randomized Controlled Trial.

Author

Bosheva M, Tokodi I, Krasnow A, Pedersen HK, Lukjancenko O, Eklund AC, Grathwohl D, Sprenger N, Berger B, and Cercamondi CI

Abstract

Background: Human milk oligosaccharides (HMOs) have important biological functions for a healthy development in early life., Objective: This study aimed to investigate gut maturation effects of an infant formula containing five HMOs (2'-fucosyllactose, 2',3-di-fucosyllactose, lacto-N-tetraose, 3'-sialyllactose, and 6'-sialyllactose)., Methods: In a multicenter study, healthy infants (7-21 days old) were randomly assigned to a standard cow's milk-based infant formula (control group, CG); the same formula with 1.5 g/L HMOs (test group 1, TG1); or with 2.5 g/L HMOs (test group 2, TG2). A human milk-fed group (HMG) was enrolled as a reference. Fecal samples collected at baseline ( n ∼150/formula group; HMG n = 60), age 3 ( n ∼140/formula group; HMG n = 65) and 6 ( n ∼115/formula group; HMG n = 60) months were analyzed for microbiome (shotgun metagenomics), metabolism, and biomarkers., Results: At both post-baseline visits, weighted UniFrac analysis indicated different microbiota compositions in the two test groups (TGs) compared to CG ( P < 0.01) with coordinates closer to that of HMG. The relative abundance of Bifidobacterium longum subsp. infantis ( B. infantis ) was higher in TGs vs. CG ( P < 0.05; except at 6 months: TG2 vs. CG P = 0.083). Bifidobacterium abundance was higher by ∼45% in TGs vs. CG at 6-month approaching HMG. At both post-baseline visits, toxigenic Clostridioides difficile abundance was 75-85% lower in TGs vs. CG ( P < 0.05) and comparable with HMG. Fecal pH was significantly lower in TGs vs. CG, and the overall organic acid profile was different in TGs vs. CG, approaching HMG. At 3 months, TGs (vs. CG) had higher secretory immunoglobulin A (sIgA) and lower alpha-1-antitrypsin ( P < 0.05). At 6 months, sIgA in TG2 vs. CG remained higher ( P < 0.05), and calprotectin was lower in TG1 ( P < 0.05) vs. CG., Conclusion: Infant formula with a specific blend of five HMOs supports the development of the intestinal immune system and gut barrier function and shifts the gut microbiome closer to that of breastfed infants with higher bifidobacteria, particularly B. infantis , and lower toxigenic Clostridioides difficile ., Clinical Trial Registration: [https://clinicaltrials.gov/ct2/show/], identifier [NCT03722550]., Competing Interests: This study received funding from Nestlé Nutrition, Société des Produits Nestlé S.A., Switzerland. DG, NS, BB, and CC were current employees of the funder. The funder had the following involvement with the study: study design, data analysis, decision to publish, and preparation of the manuscript. HP, OL, and AE were employees of Clinical Microbiomics, Denmark, which was involved in the sample and data analysis. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Bosheva, Tokodi, Krasnow, Pedersen, Lukjancenko, Eklund, Grathwohl, Sprenger, Berger, Cercamondi and 5 HMO Study Investigator Consortium.)

Published
2022
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47. Author Correction: Imidazole propionate is increased in diabetes and associated with dietary patterns and altered microbial ecology.

Author

Molinaro A, Bel Lassen P, Henricsson M, Wu H, Adriouch S, Belda E, Chakaroun R, Nielsen T, Bergh PO, Rouault C, André S, Marquet F, Andreelli F, Salem JE, Assmann K, Bastard JP, Forslund S, Le Chatelier E, Falony G, Pons N, Prifti E, Quinquis B, Roume H, Vieira-Silva S, Hansen TH, Pedersen HK, Lewinter C, Sønderskov NB, Køber L, Vestergaard H, Hansen T, Zucker JD, Galan P, Dumas ME, Raes J, Oppert JM, Letunic I, Nielsen J, Bork P, Ehrlich SD, Stumvoll M, Pedersen O, Aron-Wisnewsky J, Clément K, and Bäckhed F

Published
2020
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48. Imidazole propionate is increased in diabetes and associated with dietary patterns and altered microbial ecology.

Author

Molinaro A, Bel Lassen P, Henricsson M, Wu H, Adriouch S, Belda E, Chakaroun R, Nielsen T, Bergh PO, Rouault C, André S, Marquet F, Andreelli F, Salem JE, Assmann K, Bastard JP, Forslund S, Le Chatelier E, Falony G, Pons N, Prifti E, Quinquis B, Roume H, Vieira-Silva S, Hansen TH, Pedersen HK, Lewinter C, Sønderskov NB, Køber L, Vestergaard H, Hansen T, Zucker JD, Galan P, Dumas ME, Raes J, Oppert JM, Letunic I, Nielsen J, Bork P, Ehrlich SD, Stumvoll M, Pedersen O, Aron-Wisnewsky J, Clément K, and Bäckhed F

Subjects
Adult, Aged, Bacteria classification, Bacteria genetics, Bacteria isolation & purification, Bacteria metabolism, Cohort Studies, Diabetes Mellitus, Type 2 blood, Diabetes Mellitus, Type 2 metabolism, Female, Histidine metabolism, Humans, Male, Middle Aged, Diabetes Mellitus, Type 2 microbiology, Gastrointestinal Microbiome, Imidazoles blood
Abstract

Microbiota-host-diet interactions contribute to the development of metabolic diseases. Imidazole propionate is a novel microbially produced metabolite from histidine, which impairs glucose metabolism. Here, we show that subjects with prediabetes and diabetes in the MetaCardis cohort from three European countries have elevated serum imidazole propionate levels. Furthermore, imidazole propionate levels were increased in subjects with low bacterial gene richness and Bacteroides 2 enterotype, which have previously been associated with obesity. The Bacteroides 2 enterotype was also associated with increased abundance of the genes involved in imidazole propionate biosynthesis from dietary histidine. Since patients and controls did not differ in their histidine dietary intake, the elevated levels of imidazole propionate in type 2 diabetes likely reflects altered microbial metabolism of histidine, rather than histidine intake per se. Thus the microbiota may contribute to type 2 diabetes by generating imidazole propionate that can modulate host inflammation and metabolism.

Published
2020
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