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1. Role of human plasma metabolites in prediabetes and type 2 diabetes from the IMI-DIRECT study

Author

Sharma, Sapna, Dong, Qiuling, Haid, Mark, Adam, Jonathan, Bizzotto, Roberto, Fernandez-Tajes, Juan J., Jones, Angus G., Tura, Andrea, Artati, Anna, Prehn, Cornelia, Kastenmüller, Gabi, Koivula, Robert W., Franks, Paul W., Walker, Mark, Forgie, Ian M., Giordano, Giuseppe, Pavo, Imre, Ruetten, Hartmut, Dermitzakis, Manolis, McCarthy, Mark I., Pedersen, Oluf, Schwenk, Jochen M., Tsirigos, Konstantinos D., De Masi, Federico, Brunak, Soren, Viñuela, Ana, Mari, Andrea, McDonald, Timothy J., Kokkola, Tarja, Adamski, Jerzy, Pearson, Ewan R., and Grallert, Harald

Published
2024
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4. The Association of Cardiometabolic, Diet and Lifestyle Parameters With Plasma Glucagon-like Peptide-1: An IMI DIRECT Study.

Author

Eriksen, Rebeca, White, Margaret C, Dawed, Adem Y, Perez, Isabel Garcia, Posma, Joram M, Haid, Mark, Sharma, Sapna, Prehn, Cornelia, Thomas, E Louise, Koivula, Robert W, Bizzotto, Roberto, Mari, Andrea, Giordano, Giuseppe N, Pavo, Imre, Schwenk, Jochen M, Masi, Federico De, Tsirigos, Konstantinos D, Brunak, Søren, Viñuela, Ana, and Mahajan, Anubha

Subjects
TYPE 2 diabetes, PEOPLE with diabetes, MULTIPLE regression analysis, INSULIN resistance, FOOD consumption
Abstract

Context The role of glucagon-like peptide-1 (GLP-1) in type 2 diabetes (T2D) and obesity is not fully understood. Objective We investigate the association of cardiometabolic, diet, and lifestyle parameters on fasting and postprandial GLP-1 in people at risk of, or living with, T2D. Methods We analyzed cross-sectional data from the two Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) cohorts, cohort 1 (n = 2127) individuals at risk of diabetes; cohort 2 (n = 789) individuals with new-onset T2D. Results Our multiple regression analysis reveals that fasting total GLP-1 is associated with an insulin-resistant phenotype and observe a strong independent relationship with male sex, increased adiposity, and liver fat, particularly in the prediabetes population. In contrast, we showed that incremental GLP-1 decreases with worsening glycemia, higher adiposity, liver fat, male sex, and reduced insulin sensitivity in the prediabetes cohort. Higher fasting total GLP-1 was associated with a low intake of wholegrain, fruit, and vegetables in people with prediabetes, and with a high intake of red meat and alcohol in people with diabetes. Conclusion These studies provide novel insights into the association between fasting and incremental GLP-1, metabolic traits of diabetes and obesity, and dietary intake, and raise intriguing questions regarding the relevance of fasting GLP-1 in the pathophysiology T2D. [ABSTRACT FROM AUTHOR]

Published
2024
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5. Dietary metabolite profiling brings new insight into the relationship between nutrition and metabolic risk: An IMI DIRECT study

Author

Eriksen, Rebeca, Perez, Isabel Garcia, Posma, Joram M., Haid, Mark, Sharma, Sapna, Prehn, Cornelia, Thomas, Louise E., Koivula, Robert W., Bizzotto, Roberto, Mari, Andrea, Giordano, Giuseppe N., Pavo, Imre, Schwenk, Jochen M., De Masi, Federico, Tsirigos, Konstantinos D., Brunak, Søren, Viñuela, Ana, Mahajan, Anubha, McDonald, Timothy J., Kokkola, Tarja, Rutter, Femke, Teare, Harriet, Hansen, Tue H., Fernandez, Juan, Jones, Angus, Jennison, Chris, Walker, Mark, McCarthy, Mark I., Pedersen, Oluf, Ruetten, Hartmut, Forgie, Ian, Bell, Jimmy D., Pearson, Ewan R., Franks, Paul W., Adamski, Jerzy, Holmes, Elaine, and Frost, Gary

Published
2020
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7. Genetic studies of abdominal MRI data identify genes regulating hepcidin as major determinants of liver iron concentration

Author

Jennison, Christopher, Ehrhardt, Beate, Baum, Patrick, Schoelsch, Corinna, Freijer, Jan, Grempler, Rolf, Graefe-Mody, Ulrike, Hennige, Anita, Dings, Christiane, Lehr, Thorsten, Scherer, Nina, Sihinecich, Iryna, Pattou, Francois, Raverdi, Violeta, Caiazzo, Robert, Torres, Fanelly, Verkindt, Helene, Mari, Andrea, Tura, Andrea, Giorgino, Toni, Bizzotto, Roberto, Froguel, Philippe, Bonneford, Amelie, Canouil, Mickael, Dhennin, Veronique, Brorsson, Caroline, Brunak, Soren, De Masi, Federico, Gudmundsdóttir, Valborg, Pedersen, Helle, Banasik, Karina, Thomas, Cecilia, Sackett, Peter, Staerfeldt, Hans-Henrik, Lundgaard, Agnete, Nilsson, Birgitte, Nielsen, Agnes, Mazzoni, Gianluca, Karaderi, Tugce, Rasmussen, Simon, Johansen, Joachim, Allesøe, Rosa, Fritsche, Andreas, Thorand, Barbara, Adamski, Jurek, Grallert, Harald, Haid, Mark, Sharma, Sapna, Troll, Martina, Adam, Jonathan, Ferrer, Jorge, Eriksen, Heather, Frost, Gary, Haussler, Ragna, Hong, Mun-gwan, Schwenk, Jochen, Uhlen, Mathias, Nicolay, Claudia, Pavo, Imre, Steckel-Hamann, Birgit, Thomas, Melissa, Adragni, Kofi, Wu, Han, Hart, Leen't, Roderick, Slieker, van Leeuwen, Nienke, Dekkers, Koen, Frau, Francesca, Gassenhuber, Johann, Jablonka, Bernd, Musholt, Petra, Ruetten, Hartmut, Tillner, Joachim, Baltauss, Tania, Bernard Poenaru, Oana, de Preville, Nathalie, Rodriquez, Marianne, Arumugam, Manimozhiyan, Allin, Kristine, Engelbrechtsen, Line, Hansen, Torben, Hansen, Tue, Forman, Annemette, Jonsson, Anna, Pedersen, Oluf, Dutta, Avirup, Vogt, Josef, Vestergaard, Henrik, Laakso, Markku, Kokkola, Tarja, Kuulasmaa, Teemu, Franks, Paul, Giordano, Nick, Pomares-Millan, Hugo, Fitipaldi, Hugo, Mutie, Pascal, Klintenberg, Maria, Bergstrom, Margit, Groop, Leif, Ridderstrale, Martin, Atabaki Pasdar, Naeimeh, Deshmukh, Harshal, Heggie, Alison, Wake, Dianne, McEvoy, Donna, McVittie, Ian, Walker, Mark, Hattersley, Andrew, Hill, Anita, Jones, Angus, McDonald, Timothy, Perry, Mandy, Nice, Rachel, Hudson, Michelle, Thorne, Claire, Dermitzakis, Emmanouil, Viñuela, Ana, Cabrelli, Louise, Loftus, Heather, Dawed, Adem, Donnelly, Louise, Forgie, Ian, Pearson, Ewan, Palmer, Colin, Brown, Andrew, Koivula, Robert, Wesolowska-Andersen, Agata, Abdalla, Moustafa, McRobert, Nicky, Fernandez, Juan, Jiao, Yunlong, Robertson, Neil, Gough, Stephen, Kaye, Jane, Mourby, Miranda, Mahajan, Anubha, McCarthy, Mark, Shah, Nisha, Teare, Harriet, Holl, Reinhard, Koopman, Anitra, Rutters, Femke, Beulens, Joline, Groeneveld, Lenka, Bell, Jimmy, Thomas, Louise, Whitcher, Brandon, Wilman, Henry R., Parisinos, Constantinos A., Atabaki-Pasdar, Naeimeh, Kelly, Matt, Thomas, E. Louise, Neubauer, Stefan, Hingorani, Aroon D., Patel, Riyaz S., Hemingway, Harry, Franks, Paul W., Bell, Jimmy D., Banerjee, Rajarshi, and Yaghootkar, Hanieh

Published
2019
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11. At any Level of Adiposity, Relatively Elevated Leptin Concentrations Are Associated With Decreased Insulin Sensitivity.

Author

Chiriacò, Martina, Nesti, Lorenzo, Flyvbjerg, Allan, Golay, Alain, Nazare, Julie-Anne, Anderwald, Christian-Heinz, Mitrakou, Asimina, Bizzotto, Roberto, Mari, Andrea, and Natali, Andrea

Subjects
INSULIN sensitivity, LEPTIN, HOMEOSTASIS
Abstract

Context The impact of obesity on glucose homeostasis has high interindividual variability, which may be partially explained by different adipokine concentrations. Leptin regulates energy balance and metabolism, and although its plasma levels are proportional to fat mass, they vary significantly across individuals with the same level of adiposity. Objective We tested whether glucose homeostasis differs in subjects with similar degrees of adiposity but different leptin levels. Methods We analyzed 1290 healthy adults from the Relationship Between Insulin Sensitivity and Cardiovascular Disease study cohort (30-60 years; male/female, 577/713; body mass index [BMI], 25 ± 3 kg/m2) characterized for body composition and metabolic variables with a 75-g oral glucose tolerance test, euglycemic-hyperinsulinemic clamp, β-cell function, and lipidomics. Results Individuals were divided into relatively high and low leptin (RHL and RLL) if they were above or below the sex-specific leptin-fat mass (%) regression. Despite similar glucose tolerance, RHL showed markedly higher fasting and oral glucose tolerance test insulin concentration (+30% and +29%, respectively; P <.0001) and secretion (+17% and +11%, respectively; P <.0001). Regardless of BMI, RHL individuals had lower whole-body (−17-23%, P <.0001) and adipose tissue insulin sensitivity (−24%, P <.0001) compared with RLL. Notably, lean RHL individuals showed similar insulin sensitivity and β-cell function to RLL individuals with overweight/obesity. Conclusion Subjects with leptin levels that are inappropriately elevated for their fat mass show whole-body/adipose tissue insulin resistance and hyperinsulinemia, regardless of BMI. [ABSTRACT FROM AUTHOR]

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

Author

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

Abstract

The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug–omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug–drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities., Correction in DOI 10.1038/s41587-023-01805-9QC 20230626

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

Author

Brown, Andrew A., Fernandez-Tajes, Juan J., Hong, Mun gwan, Brorsson, Caroline A., Koivula, Robert W., Davtian, David, Dupuis, Théo, Sartori, Ambra, Michalettou, Theodora Dafni, Forgie, Ian M., Adam, Jonathan, Allin, Kristine H., Caiazzo, Robert, Cederberg, Henna, De Masi, Federico, Elders, Petra J.M., Giordano, Giuseppe N., Haid, Mark, Hansen, Torben, Hansen, Tue H., Hattersley, Andrew T., Heggie, Alison J., Howald, Cédric, Jones, Angus G., Kokkola, Tarja, Laakso, Markku, Mahajan, Anubha, Mari, Andrea, McDonald, Timothy J., McEvoy, Donna, Mourby, Miranda, Musholt, Petra B., Nilsson, Birgitte, Pattou, Francois, Penet, Deborah, Raverdy, Violeta, Ridderstråle, Martin, Romano, Luciana, Rutters, Femke, Sharma, Sapna, Teare, Harriet, ‘t Hart, Leen, Tsirigos, Konstantinos D., Vangipurapu, Jagadish, Vestergaard, Henrik, Brunak, Søren, Franks, Paul W., Frost, Gary, Grallert, Harald, Jablonka, Bernd, McCarthy, Mark I., Pavo, Imre, Pedersen, Oluf, Ruetten, Hartmut, Walker, Mark, Adragni, Kofi, Allesøe, Rosa Lundbye L., Artati, Anna A., Arumugam, Manimozhiyan, Atabaki-Pasdar, Naeimeh, Baltauss, Tania, Banasik, Karina, Barnett, Anna L., Baum, Patrick, Bell, Jimmy D., Beulens, Joline W., Bianzano, Susanna B., Bizzotto, Roberto, Bonnefond, Amelie, Cabrelli, Louise, Dale, Matilda, Dawed, Adem Y., de Preville, Nathalie, Dekkers, Koen F., Deshmukh, Harshal A., Dings, Christiane, Donnelly, Louise, Dutta, Avirup, Ehrhardt, Beate, Engelbrechtsen, Line, Eriksen, Rebeca, Fan, Yong, Ferrer, Jorge, Fitipaldi, Hugo, Forman, Annemette, Fritsche, Andreas, Froguel, Philippe, Gassenhuber, Johann, Gough, Stephen, Graefe-Mody, Ulrike, Grempler, Rolf, Groeneveld, Lenka, Groop, Leif, Gudmundsdóttir, Valborg, Gupta, Ramneek, Hennige, Anita M.H., Hill, Anita V., Holl, Reinhard W., Hudson, Michelle, Jacobsen, Ulrik Plesner, Jennison, Christopher, Johansen, Joachim, Jonsson, Anna, Karaderi, Tugce, Kaye, Jane, Kennedy, Gwen, Klintenberg, Maria, Kuulasmaa, Teemu, Lehr, Thorsten, Loftus, Heather, Lundgaard, Agnete Troen T., Mazzoni, Gianluca, McRobert, Nicky, McVittie, Ian, Nice, Rachel, Nicolay, Claudia, Nijpels, Giel, Palmer, Colin N., Pedersen, Helle K., Perry, Mandy H., Pomares-Millan, Hugo, Prehn, Cornelia P., Ramisch, Anna, Rasmussen, Simon, Robertson, Neil, Rodriquez, Marianne, Sackett, Peter, Scherer, Nina, Shah, Nisha, Sihinevich, Iryna, Slieker, Roderick C., Sondertoft, Nadja B., Steckel-Hamann, Birgit, Thomas, Melissa K., Thomas, Cecilia Engel E., Thomas, Elizabeth Louise L., Thorand, Barbara, Thorne, Claire E., Tillner, Joachim, Tura, Andrea, Uhlen, Mathias, van Leeuwen, Nienke, van Oort, Sabine, Verkindt, Helene, Vogt, Josef, Wad Sackett, Peter W., Wesolowska-Andersen, Agata, Whitcher, Brandon, White, Margaret W., Adamski, Jerzy, Schwenk, Jochen M., Pearson, Ewan R., Dermitzakis, Emmanouil T., Viñuela, Ana, Brown, Andrew A., Fernandez-Tajes, Juan J., Hong, Mun gwan, Brorsson, Caroline A., Koivula, Robert W., Davtian, David, Dupuis, Théo, Sartori, Ambra, Michalettou, Theodora Dafni, Forgie, Ian M., Adam, Jonathan, Allin, Kristine H., Caiazzo, Robert, Cederberg, Henna, De Masi, Federico, Elders, Petra J.M., Giordano, Giuseppe N., Haid, Mark, Hansen, Torben, Hansen, Tue H., Hattersley, Andrew T., Heggie, Alison J., Howald, Cédric, Jones, Angus G., Kokkola, Tarja, Laakso, Markku, Mahajan, Anubha, Mari, Andrea, McDonald, Timothy J., McEvoy, Donna, Mourby, Miranda, Musholt, Petra B., Nilsson, Birgitte, Pattou, Francois, Penet, Deborah, Raverdy, Violeta, Ridderstråle, Martin, Romano, Luciana, Rutters, Femke, Sharma, Sapna, Teare, Harriet, ‘t Hart, Leen, Tsirigos, Konstantinos D., Vangipurapu, Jagadish, Vestergaard, Henrik, Brunak, Søren, Franks, Paul W., Frost, Gary, Grallert, Harald, Jablonka, Bernd, McCarthy, Mark I., Pavo, Imre, Pedersen, Oluf, Ruetten, Hartmut, Walker, Mark, Adragni, Kofi, Allesøe, Rosa Lundbye L., Artati, Anna A., Arumugam, Manimozhiyan, Atabaki-Pasdar, Naeimeh, Baltauss, Tania, Banasik, Karina, Barnett, Anna L., Baum, Patrick, Bell, Jimmy D., Beulens, Joline W., Bianzano, Susanna B., Bizzotto, Roberto, Bonnefond, Amelie, Cabrelli, Louise, Dale, Matilda, Dawed, Adem Y., de Preville, Nathalie, Dekkers, Koen F., Deshmukh, Harshal A., Dings, Christiane, Donnelly, Louise, Dutta, Avirup, Ehrhardt, Beate, Engelbrechtsen, Line, Eriksen, Rebeca, Fan, Yong, Ferrer, Jorge, Fitipaldi, Hugo, Forman, Annemette, Fritsche, Andreas, Froguel, Philippe, Gassenhuber, Johann, Gough, Stephen, Graefe-Mody, Ulrike, Grempler, Rolf, Groeneveld, Lenka, Groop, Leif, Gudmundsdóttir, Valborg, Gupta, Ramneek, Hennige, Anita M.H., Hill, Anita V., Holl, Reinhard W., Hudson, Michelle, Jacobsen, Ulrik Plesner, Jennison, Christopher, Johansen, Joachim, Jonsson, Anna, Karaderi, Tugce, Kaye, Jane, Kennedy, Gwen, Klintenberg, Maria, Kuulasmaa, Teemu, Lehr, Thorsten, Loftus, Heather, Lundgaard, Agnete Troen T., Mazzoni, Gianluca, McRobert, Nicky, McVittie, Ian, Nice, Rachel, Nicolay, Claudia, Nijpels, Giel, Palmer, Colin N., Pedersen, Helle K., Perry, Mandy H., Pomares-Millan, Hugo, Prehn, Cornelia P., Ramisch, Anna, Rasmussen, Simon, Robertson, Neil, Rodriquez, Marianne, Sackett, Peter, Scherer, Nina, Shah, Nisha, Sihinevich, Iryna, Slieker, Roderick C., Sondertoft, Nadja B., Steckel-Hamann, Birgit, Thomas, Melissa K., Thomas, Cecilia Engel E., Thomas, Elizabeth Louise L., Thorand, Barbara, Thorne, Claire E., Tillner, Joachim, Tura, Andrea, Uhlen, Mathias, van Leeuwen, Nienke, van Oort, Sabine, Verkindt, Helene, Vogt, Josef, Wad Sackett, Peter W., Wesolowska-Andersen, Agata, Whitcher, Brandon, White, Margaret W., Adamski, Jerzy, Schwenk, Jochen M., Pearson, Ewan R., Dermitzakis, Emmanouil T., and Viñuela, Ana

Abstract

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

Published
2023

14. 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. Processes Underlying Glycemic Deterioration in Type 2 Diabetes : An IMI DIRECT Study

Author

Bizzotto, Roberto, Schwenk, Jochen M., Mari, Andrea, Bizzotto, Roberto, Schwenk, Jochen M., and Mari, Andrea

Abstract

OBJECTIVE We investigated the processes underlying glycemic deterioration in type 2 diabetes (T2D). RESEARCH DESIGN AND METHODS A total of 732 recently diagnosed patients with T2D from the Innovative Medicines Initiative Diabetes Research on Patient Stratification (IMI DIRECT) study were extensively phenotyped over 3 years, including measures of insulin sensitivity (OGIS), beta-cell glucose sensitivity (GS), and insulin clearance (CLIm) from mixed meal tests, liver enzymes, lipid profiles, and baseline regional fat from MRI. The associations between the longitudinal metabolic patterns and HbA(1c) deterioration, adjusted for changes in BMI and in diabetes medications, were assessed via stepwise multivariable linear and logistic regression. RESULTS Faster HbA(1c) progression was independently associated with faster deterioration of OGIS and GS and increasing CLIm; visceral or liver fat, HDL-cholesterol, and triglycerides had further independent, though weaker, roles (R-2 = 0.38). A subgroup of patients with a markedly higher progression rate (fast progressors) was clearly distinguishable considering these variables only (discrimination capacity from area under the receiver operating characteristic = 0.94). The proportion of fast progressors was reduced from 56% to 8-10% in subgroups in which only one trait among OGIS, GS, and CLIm was relatively stable (odds ratios 0.07-0.09). T2D polygenic risk score and baseline pancreatic fat, glucagon-like peptide 1, glucagon, diet, and physical activity did not show an independent role. CONCLUSIONS Deteriorating insulin sensitivity and beta-cell function, increasing insulin clearance, high visceral or liver fat, and worsening of the lipid profile are the crucial factors mediating glycemic deterioration of patients with T2D in the initial phase of the disease. Stabilization of a single trait among insulin sensitivity, beta-cell function, and insulin clearance may be relevant to prevent progression., QC 20210211

Published
2021
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19. Processes Underlying Glycemic Deterioration in Type 2 Diabetes:An IMI DIRECT Study

Author

Bizzotto, Roberto, Jennison, Christopher, Jones, Angus G., Kurbasic, Azra, Tura, Andrea, Kennedy, Gwen, Bell, Jimmy D., Thomas, E. Louise, Frost, Gary, Eriksen, Rebeca, Koivula, Robert W., Brage, Soren, Kaye, Jane, Hattersley, Andrew T., Heggie, Alison, McEvoy, Donna, 't Hart, Leen M., Beulens, Joline W., Elders, Petra, Musholt, Petra B., Ridderstrale, Martin, Hansen, Tue H., Allin, Kristine H., Hansen, Torben, Vestergaard, Henrik, Lundgaard, Agnete T., Thomsen, Henrik S., De Masi, Federico, Tsirigos, Konstantinos D., Brunak, Søren, Vinuela, Ana, Mahajan, Anubha, McDonald, Timothy J., Kokkola, Tarja, Forgie, Ian M., Giordano, Giuseppe N., Pavo, Imre, Ruetten, Hartmut, Dermitzakis, Emmanouil, McCarthy, Mark I., Pedersen, Oluf, Schwenk, Jochen M., Adamski, Jerzy, Franks, Paul W., Walker, Mark, Pearson, Ewan R., Mari, Andrea, Bizzotto, Roberto, Jennison, Christopher, Jones, Angus G., Kurbasic, Azra, Tura, Andrea, Kennedy, Gwen, Bell, Jimmy D., Thomas, E. Louise, Frost, Gary, Eriksen, Rebeca, Koivula, Robert W., Brage, Soren, Kaye, Jane, Hattersley, Andrew T., Heggie, Alison, McEvoy, Donna, 't Hart, Leen M., Beulens, Joline W., Elders, Petra, Musholt, Petra B., Ridderstrale, Martin, Hansen, Tue H., Allin, Kristine H., Hansen, Torben, Vestergaard, Henrik, Lundgaard, Agnete T., Thomsen, Henrik S., De Masi, Federico, Tsirigos, Konstantinos D., Brunak, Søren, Vinuela, Ana, Mahajan, Anubha, McDonald, Timothy J., Kokkola, Tarja, Forgie, Ian M., Giordano, Giuseppe N., Pavo, Imre, Ruetten, Hartmut, Dermitzakis, Emmanouil, McCarthy, Mark I., Pedersen, Oluf, Schwenk, Jochen M., Adamski, Jerzy, Franks, Paul W., Walker, Mark, Pearson, Ewan R., and Mari, Andrea

Abstract

OBJECTIVEWe investigated the processes underlying glycemic deterioration in type 2 diabetes (T2D).RESEARCH DESIGN AND METHODSA total of 732 recently diagnosed patients with T2D from the Innovative Medicines Initiative Diabetes Research on Patient Stratification (IMI DIRECT) study were extensively phenotyped over 3 years, including measures of insulin sensitivity (OGIS), beta-cell glucose sensitivity (GS), and insulin clearance (CLIm) from mixed meal tests, liver enzymes, lipid profiles, and baseline regional fat from MRI. The associations between the longitudinal metabolic patterns and HbA(1c) deterioration, adjusted for changes in BMI and in diabetes medications, were assessed via stepwise multivariable linear and logistic regression.RESULTSFaster HbA(1c) progression was independently associated with faster deterioration of OGIS and GS and increasing CLIm; visceral or liver fat, HDL-cholesterol, and triglycerides had further independent, though weaker, roles (R-2 = 0.38). A subgroup of patients with a markedly higher progression rate (fast progressors) was clearly distinguishable considering these variables only (discrimination capacity from area under the receiver operating characteristic = 0.94). The proportion of fast progressors was reduced from 56% to 8-10% in subgroups in which only one trait among OGIS, GS, and CLIm was relatively stable (odds ratios 0.07-0.09). T2D polygenic risk score and baseline pancreatic fat, glucagon-like peptide 1, glucagon, diet, and physical activity did not show an independent role.CONCLUSIONSDeteriorating insulin sensitivity and beta-cell function, increasing insulin clearance, high visceral or liver fat, and worsening of the lipid profile are the crucial factors mediating glycemic deterioration of patients with T2D in the initial phase of the disease. Stabilization of a single trait among insulin sensitivity, beta-cell function, and insulin clearance may be relevant to prevent

Published
2021

22. Dietary metabolite profiling brings new insight into the relationship between nutrition and metabolic risk:An IMI DIRECT study

Author

Eriksen, Rebeca, Perez, Isabel Garcia, Posma, Joram M, Haid, Mark, Sharma, Sapna, Prehn, Cornelia, Thomas, Louise E, Koivula, Robert W, Bizzotto, Roberto, Mari, Andrea, Giordano, Giuseppe N, Pavo, Imre, Schwenk, Jochen M, De Masi, Federico, Tsirigos, Konstantinos D, Brunak, Søren, Viñuela, Ana, Mahajan, Anubha, McDonald, Timothy J, Kokkola, Tarja, Rutter, Femke, Teare, Harriet, Hansen, Tue H, Fernandez, Juan, Jones, Angus, Jennison, Chris, Walker, Mark, McCarthy, Mark I, Pedersen, Oluf, Ruetten, Hartmut, Forgie, Ian, Bell, Jimmy D, Pearson, Ewan R, Franks, Paul W, Adamski, Jerzy, Holmes, Elaine, Frost, Gary, Eriksen, Rebeca, Perez, Isabel Garcia, Posma, Joram M, Haid, Mark, Sharma, Sapna, Prehn, Cornelia, Thomas, Louise E, Koivula, Robert W, Bizzotto, Roberto, Mari, Andrea, Giordano, Giuseppe N, Pavo, Imre, Schwenk, Jochen M, De Masi, Federico, Tsirigos, Konstantinos D, Brunak, Søren, Viñuela, Ana, Mahajan, Anubha, McDonald, Timothy J, Kokkola, Tarja, Rutter, Femke, Teare, Harriet, Hansen, Tue H, Fernandez, Juan, Jones, Angus, Jennison, Chris, Walker, Mark, McCarthy, Mark I, Pedersen, Oluf, Ruetten, Hartmut, Forgie, Ian, Bell, Jimmy D, Pearson, Ewan R, Franks, Paul W, Adamski, Jerzy, Holmes, Elaine, and Frost, Gary

Abstract

BACKGROUND: Dietary advice remains the cornerstone of prevention and management of type 2 diabetes (T2D). However, understanding the efficacy of dietary interventions is confounded by the challenges inherent in assessing free living diet. Here we profiled dietary metabolites to investigate glycaemic deterioration and cardiometabolic risk in people at risk of or living with T2D.METHODS: We analysed data from plasma collected at baseline and 18-month follow-up in individuals from the Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) cohort 1 n = 403 individuals with normal or impaired glucose regulation (prediabetic) and cohort 2 n = 458 individuals with new onset of T2D. A dietary metabolite profile model (Tpred) was constructed using multivariable regression of 113 plasma metabolites obtained from targeted metabolomics assays. The continuous Tpred score was used to explore the relationships between diet, glycaemic deterioration and cardio-metabolic risk via multiple linear regression models.FINDINGS: A higher Tpred score was associated with healthier diets high in wholegrain (β=3.36 g, 95% CI 0.31, 6.40 and β=2.82 g, 95% CI 0.06, 5.57) and lower energy intake (β=-75.53 kcal, 95% CI -144.71, -2.35 and β=-122.51 kcal, 95% CI -186.56, -38.46), and saturated fat (β=-0.92 g, 95% CI -1.56, -0.28 and β=-0.98 g, 95% CI -1.53, -0.42 g), respectively for cohort 1 and 2. In both cohorts a higher Tpred score was also associated with lower total body adiposity and favourable lipid profiles HDL-cholesterol (β=0.07 mmol/L, 95% CI 0.03, 0.1), (β=0.08 mmol/L, 95% CI 0.04, 0.1), and triglycerides (β=-0.1 mmol/L, 95% CI -0.2, -0.03), (β=-0.2 mmol/L, 95% CI -0.3, -0.09), respectively for cohort 1 and 2. In cohort 2, the Tpred score was negatively associated with liver fat (β=-0.74%, 95% CI -0.67, -0.81), and lower fasting concentrations of HbA1c (β=-0.9 mmol/mol, 95% CI -1.5, -0.1), glucose (β=-0.2 mmol/L, 95% CI -0.4, -0.05) an

Published
2020

23. Processes Underlying Glycemic Deterioration in Type 2 Diabetes: An IMI DIRECT Study

Author

Bizzotto, Roberto, primary, Jennison, Christopher, primary, Jones, Angus G, primary, Kurbasic, Azra, primary, Tura, Andrea, primary, Kennedy, Gwen, primary, Bell, Jimmy D, primary, Thomas, Elizabeth L, primary, Frost, Gary, primary, Eriksen, Rebeca, primary, Koivula, Robert W, primary, Brage, Soren, primary, Kaye, Jane, primary, Hattersley, Andrew T, primary, Heggie, Alison, primary, McEvoy, Donna, primary, Hart, Leen M ’t, primary, Beulens, Joline W, primary, Elders, Petra, primary, Musholt, Petra B, primary, Ridderstråle, Martin, primary, Hansen, Tue H, primary, Allin, Kristine H, primary, Hansen, Torben, primary, Vestergaard, Henrik, primary, Lundgaard, Agnete T, primary, Thomsen, Henrik S, primary, Masi, Federico De, primary, Tsirigos, Konstantinos D, primary, Brunak, Søren, primary, Viñuela, Ana, primary, Mahajan, Anubha, primary, McDonald, Timothy J, primary, Kokkola, Tarja, primary, Forgie, Ian M, primary, Giordano, Giuseppe N, primary, Pavo, Imre, primary, Ruetten, Hartmut, primary, Dermitzakis, Emmanouil, primary, McCarthy, Mark I, primary, Pedersen, Oluf, primary, Schwenk, Jochen M, primary, Adamski, Jerzy, primary, Franks, Paul W, primary, Walker, Mark, primary, Pearson, Ewan R, primary, Mari, Andrea, primary, and consortium, the IMI DIRECT, primary

Published
2020
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25. Genetic studies of abdominal MRI data identify genes regulating hepcidin as major determinants of liver iron concentration

Author

Wilman, Henry R., Parisinos, Constantinos A., Atabaki-Pasdar, Naeimeh, Kelly, Matt, Thomas, E. Louise, Neubauer, Stefan, Jennison, Christopher, Ehrhardt, Beate, Baum, Patrick, Schoelsch, Corinna, Freijer, Jan, Grempler, Rolf, Graefe-Mody, Ulrike, Hennige, Anita, Dings, Christiane, Lehr, Thorsten, Scherer, Nina, Sihinecich, Iryna, Pattou, Francois, Raverdi, Violeta, Caiazzo, Robert, Torres, Fanelly, Verkindt, Helene, Mari, Andrea, Tura, Andrea, Giorgino, Toni, Bizzotto, Roberto, Froguel, Philippe, Bonneford, Amelie, Canouil, Mickael, Dhennin, Veronique, Brorsson, Caroline, Brunak, Soren, De Masi, Federico, Gudmundsdóttir, Valborg, Pedersen, Helle, Banasik, Karina, Thomas, Cecilia, Sackett, Peter, Staerfeldt, Hans Henrik, Lundgaard, Agnete, Nilsson, Birgitte, Nielsen, Agnes, Mazzoni, Gianluca, Karaderi, Tugce, Rasmussen, Simon, Johansen, Joachim, Allesøe, Rosa, Fritsche, Andreas, Thorand, Barbara, Adamski, Jurek, Grallert, Harald, Haid, Mark, Sharma, Sapna, Troll, Martina, Adam, Jonathan, Ferrer, Jorge, Eriksen, Heather, Frost, Gary, Haussler, Ragna, Hong, Mun gwan, Schwenk, Jochen, Uhlen, Mathias, Nicolay, Claudia, Pavo, Imre, Steckel-Hamann, Birgit, Thomas, Melissa, Adragni, Kofi, Wu, Han, Hart, Leen't, Roderick, Slieker, van Leeuwen, Nienke, Dekkers, Koen, Frau, Francesca, Gassenhuber, Johann, Jablonka, Bernd, Musholt, Petra, Ruetten, Hartmut, Tillner, Joachim, Baltauss, Tania, Bernard Poenaru, Oana, de Preville, Nathalie, Rodriquez, Marianne, Arumugam, Manimozhiyan, Allin, Kristine, Engelbrechtsen, Line, Hansen, Torben, Hansen, Tue, Forman, Annemette, Jonsson, Anna, Pedersen, Oluf, Dutta, Avirup, Vogt, Josef, Vestergaard, Henrik, Laakso, Markku, Kokkola, Tarja, Kuulasmaa, Teemu, Franks, Paul, Giordano, Nick, and Pomares-Millan, Hugo

Subjects
Genome-wide association study, Magnetic resonance imaging, Metabolism, Iron, Genetics, Metabolic syndrome
Abstract

Background & Aims: Excess liver iron content is common and is linked to the risk of hepatic and extrahepatic diseases. We aimed to identify genetic variants influencing liver iron content and use genetics to understand its link to other traits and diseases. Methods: First, we performed a genome-wide association study (GWAS) in 8,289 individuals from UK Biobank, whose liver iron level had been quantified by magnetic resonance imaging, before validating our findings in an independent cohort (n = 1,513 from IMI DIRECT). Second, we used Mendelian randomisation to test the causal effects of 25 predominantly metabolic traits on liver iron content. Third, we tested phenome-wide associations between liver iron variants and 770 traits and disease outcomes. Results: We identified 3 independent genetic variants (rs1800562 [C282Y] and rs1799945 [H63D] in HFE and rs855791 [V736A] in TMPRSS6) associated with liver iron content that reached the GWAS significance threshold (p

Published
2019
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26. Genetic studies of abdominal MRI data identify genes regulating hepcidin as major determinants of liver iron concentration

Author

Wilman, Henry R., primary, Parisinos, Constantinos A., additional, Atabaki-Pasdar, Naeimeh, additional, Kelly, Matt, additional, Thomas, E. Louise, additional, Neubauer, Stefan, additional, Mahajan, Anubha, additional, Hingorani, Aroon D., additional, Patel, Riyaz S., additional, Hemingway, Harry, additional, Franks, Paul W., additional, Bell, Jimmy D., additional, Banerjee, Rajarshi, additional, Yaghootkar, Hanieh, additional, Jennison, Christopher, additional, Ehrhardt, Beate, additional, Baum, Patrick, additional, Schoelsch, Corinna, additional, Freijer, Jan, additional, Grempler, Rolf, additional, Graefe-Mody, Ulrike, additional, Hennige, Anita, additional, Dings, Christiane, additional, Lehr, Thorsten, additional, Scherer, Nina, additional, Sihinecich, Iryna, additional, Pattou, Francois, additional, Raverdi, Violeta, additional, Caiazzo, Robert, additional, Torres, Fanelly, additional, Verkindt, Helene, additional, Mari, Andrea, additional, Tura, Andrea, additional, Giorgino, Toni, additional, Bizzotto, Roberto, additional, Froguel, Philippe, additional, Bonneford, Amelie, additional, Canouil, Mickael, additional, Dhennin, Veronique, additional, Brorsson, Caroline, additional, Brunak, Soren, additional, De Masi, Federico, additional, Gudmundsdóttir, Valborg, additional, Pedersen, Helle, additional, Banasik, Karina, additional, Thomas, Cecilia, additional, Sackett, Peter, additional, Staerfeldt, Hans-Henrik, additional, Lundgaard, Agnete, additional, Nilsson, Birgitte, additional, Nielsen, Agnes, additional, Mazzoni, Gianluca, additional, Karaderi, Tugce, additional, Rasmussen, Simon, additional, Johansen, Joachim, additional, Allesøe, Rosa, additional, Fritsche, Andreas, additional, Thorand, Barbara, additional, Adamski, Jurek, additional, Grallert, Harald, additional, Haid, Mark, additional, Sharma, Sapna, additional, Troll, Martina, additional, Adam, Jonathan, additional, Ferrer, Jorge, additional, Eriksen, Heather, additional, Frost, Gary, additional, Haussler, Ragna, additional, Hong, Mun-gwan, additional, Schwenk, Jochen, additional, Uhlen, Mathias, additional, Nicolay, Claudia, additional, Pavo, Imre, additional, Steckel-Hamann, Birgit, additional, Thomas, Melissa, additional, Adragni, Kofi, additional, Wu, Han, additional, Hart, Leen't, additional, Roderick, Slieker, additional, van Leeuwen, Nienke, additional, Dekkers, Koen, additional, Frau, Francesca, additional, Gassenhuber, Johann, additional, Jablonka, Bernd, additional, Musholt, Petra, additional, Ruetten, Hartmut, additional, Tillner, Joachim, additional, Baltauss, Tania, additional, Bernard Poenaru, Oana, additional, de Preville, Nathalie, additional, Rodriquez, Marianne, additional, Arumugam, Manimozhiyan, additional, Allin, Kristine, additional, Engelbrechtsen, Line, additional, Hansen, Torben, additional, Hansen, Tue, additional, Forman, Annemette, additional, Jonsson, Anna, additional, Pedersen, Oluf, additional, Dutta, Avirup, additional, Vogt, Josef, additional, Vestergaard, Henrik, additional, Laakso, Markku, additional, Kokkola, Tarja, additional, Kuulasmaa, Teemu, additional, Franks, Paul, additional, Giordano, Nick, additional, Pomares-Millan, Hugo, additional, Fitipaldi, Hugo, additional, Mutie, Pascal, additional, Klintenberg, Maria, additional, Bergstrom, Margit, additional, Groop, Leif, additional, Ridderstrale, Martin, additional, Atabaki Pasdar, Naeimeh, additional, Deshmukh, Harshal, additional, Heggie, Alison, additional, Wake, Dianne, additional, McEvoy, Donna, additional, McVittie, Ian, additional, Walker, Mark, additional, Hattersley, Andrew, additional, Hill, Anita, additional, Jones, Angus, additional, McDonald, Timothy, additional, Perry, Mandy, additional, Nice, Rachel, additional, Hudson, Michelle, additional, Thorne, Claire, additional, Dermitzakis, Emmanouil, additional, Viñuela, Ana, additional, Cabrelli, Louise, additional, Loftus, Heather, additional, Dawed, Adem, additional, Donnelly, Louise, additional, Forgie, Ian, additional, Pearson, Ewan, additional, Palmer, Colin, additional, Brown, Andrew, additional, Koivula, Robert, additional, Wesolowska-Andersen, Agata, additional, Abdalla, Moustafa, additional, McRobert, Nicky, additional, Fernandez, Juan, additional, Jiao, Yunlong, additional, Robertson, Neil, additional, Gough, Stephen, additional, Kaye, Jane, additional, Mourby, Miranda, additional, McCarthy, Mark, additional, Shah, Nisha, additional, Teare, Harriet, additional, Holl, Reinhard, additional, Koopman, Anitra, additional, Rutters, Femke, additional, Beulens, Joline, additional, Groeneveld, Lenka, additional, Bell, Jimmy, additional, Thomas, Louise, additional, and Whitcher, Brandon, additional

Published
2019
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30. Model Description Language (MDL) : A Standard for Modeling and Simulation

Author

Smith, Mike K., Moodie, Stuart L., Bizzotto, Roberto, Blaudez, Eric, Borella, Elisa, Carrara, Letizia, Chan, Phylinda, Chenel, Marylore, Comets, Emmanuelle, Gieschke, Ronald, Harling, Kajsa, Harnisch, Lutz, Hartung, Niklas, Hooker, Andrew C., Karlsson, Mats O., Kaye, Richard, Kloft, Charlotte, Kokash, Natallia, Lavielle, Marc, Lestini, Giulia, Magni, Paolo, Mari, Andrea, Mentre, France, Muselle, Chris, Nordgren, Rikard, Nyberg, Henrik B., Parra-Guillen, Zinnia P., Pasotti, Lorenzo, Rode-Kristensen, Niels, Sardu, Maria L., Smith, Gareth R., Swat, Maciej J., Terranova, Nadia, Yngman, Gunnar, Yvon, Florent, Holford, Nick H, Smith, Mike K., Moodie, Stuart L., Bizzotto, Roberto, Blaudez, Eric, Borella, Elisa, Carrara, Letizia, Chan, Phylinda, Chenel, Marylore, Comets, Emmanuelle, Gieschke, Ronald, Harling, Kajsa, Harnisch, Lutz, Hartung, Niklas, Hooker, Andrew C., Karlsson, Mats O., Kaye, Richard, Kloft, Charlotte, Kokash, Natallia, Lavielle, Marc, Lestini, Giulia, Magni, Paolo, Mari, Andrea, Mentre, France, Muselle, Chris, Nordgren, Rikard, Nyberg, Henrik B., Parra-Guillen, Zinnia P., Pasotti, Lorenzo, Rode-Kristensen, Niels, Sardu, Maria L., Smith, Gareth R., Swat, Maciej J., Terranova, Nadia, Yngman, Gunnar, Yvon, Florent, and Holford, Nick H

Abstract

Recent work on Model Informed Drug Discovery and Development (MID3) has noted the need for clarity in model description used in quantitative disciplines such as pharmacology and statistics. 1-3 Currently, models are encoded in a variety of computer languages and are shared through publications that rarely include original code and generally lack reproducibility. The DDMoRe Model Description Language (MDL) has been developed primarily as a language standard to facilitate sharing knowledge and understanding of models.

Published
2017
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35. Inhibition of sweet chemosensory receptors alters insulin responses during glucose ingestion in healthy adults: a randomized crossover interventional study.

Author

Azari, Elnaz Karimian, Smith, Kathleen R., Osborne, Timothy F., Pratley, Richard E., Kyriazis, George A., Fanchao Yi, Bizzotto, Roberto, and Mari, Andrea

Subjects
BLOOD sugar analysis, TONGUE physiology, ACETAMINOPHEN, BLOOD sugar, C-peptide, CELL receptors, CLINICAL trials, CROSSOVER trials, DIGESTION, GASTROINTESTINAL motility, GLUCAGON, GLUCOSE, GLUCOSE tolerance tests, INSULIN, INSULIN resistance, MATHEMATICS, PEPTIDES, PROBABILITY theory, SACCHARIN, STATISTICAL sampling, TASTE, GLUCAGON-like peptide 1, BODY mass index, RANDOMIZED controlled trials, GLUCAGON-like peptides, DESCRIPTIVE statistics
Abstract

Background: Glucose is a natural ligand for sweet taste receptors (STRs) that are expressed on the tongue and in the gastrointestinal tract. Whether STRs directly contribute to the regulation of glucose homeostasis in response to glucose ingestion is unclear. Objective: We sought to determine the metabolic effects of the pharmacologic inhibition of STRs in response to an oral glucose load in healthy lean participants. Design: Ten healthy lean participants with a body mass index (in kg/m2) of 22.4 ± 0.8 were subjected to an oral-glucose-tolerance test (OGTT) on 4 separate days with the use of a randomized crossover design. Ten minutes before the 75-g OGTT, participants consumed a preload solution of either 300 parts per million (ppm) saccharin or water with or without the addition of 500 ppm lactisole, a humanspecific inhibitor of STRs. When present, lactisole was included in both the preload and OGTT solutions. We assessed plasma responses of glucose, insulin, C-peptide, glucagon, glucagon-like peptides 1 and 2, gastric inhibitory peptide, acetaminophen, and 3-O-methylglucose. With the use of mathematical modeling, we estimated gastric emptying, glucose absorption, β-cell function, insulin sensitivity and clearance, and the portal insulin:glucagon ratio. Results: The addition of lactisole to the OGTT caused increases in the plasma responses of insulin (P = 0.012), C-peptide (P = 0.004), and the insulin secretory rate (P = 0.020) compared with the control OGTT. The addition of lactisole also caused a slight reduction in the insulin sensitivity index independent of prior saccharin consumption (P < 0.025). The ingestion of saccharin before the OGTT did not alter any of the measured variables but eliminated the effects of lactisole on the OGTT. Conclusion: The pharmacologic inhibition of STRs in the gastrointestinal tract alters insulin responses during an oral glucose challenge in lean healthy participants. This trial was registered at clinicaltrials.gov as NCT02835859. [ABSTRACT FROM AUTHOR]

Published
2017
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36. 189-OR: Plasma Proteome Profiling of Prediabetes and Diabetes Progression: An IMI Direct Study.

Author

HONG, MUN-GWAN, VIÑUELA, ANA, HÄUSSLER, RAGNA S., DALE, MATILDA, KOIVULA, ROBERT W., FERNANDEZ-TAJES, JUAN, MAHAJAN, ANUBHA, BIZZOTTO, ROBERTO, MARI, ANDREA, DERMITZAKIS, EMMANOUIL, MCCARTHY, MARK, FRANKS, PAUL W., PEARSON, EWAN, and SCHWENK, JOCHEN M.

Abstract

Plasma proteins can provide valuable insights on human health and disease states. Within the framework of the EU IMI project DIRECT (https://www.direct-diabetes.org), we used a set of affinity proteomic methods to profile > 3100 study participants at baseline. Multiplexed assays quantified more than 600 unique proteins in EDTA plasma from this multi-center cohort that included 2300 subjects at risk of developing T2D (HbA1c ~ 6-6.5%) as well as 800 with early T2D (HbA1c > 6.5%). Using extensive clinical and other omics metadata available, the aim of the investigation was to identify plasma proteins associated with baseline traits. An initial analysis highlighted the importance of considering sample-related and pre-analytical variables as possible confounders in the data analysis. Hence, we used linear mixed models that included several parameters such as age, sex, study center and collection date. Next, we defined proteins associating with any of the >50 quantitative clinical traits at baseline. We found more than 300 proteins in plasma that were associated with diabetes related traits (adjusted p-value < 0.0001), many of which were prominently associated with BMI. The shortlisted candidates included leptin which associates with waist circumference and BMI; IGFBP1 and IGFBP2 to Matsuda; adiponectin to basal insulin secretion rate and fasting HDL; LDL receptor proteins to fasting triglycerides; APOM to fasting cholesterol; or IL8 and MCP-1 to fasting AST. In addition, we performed pQTL analysis to assess any connection between the protein values in plasma and genetic variants. We observed >400 cis-pQTLs (q-value < 0.05), such as for APOM (rs2736163, p = 5.15 e-24), which illustrated that many of the studied protein profiles are affected by a genetic component. With follow-up samples collected 3-4 years after starting the study, the baseline values will serve as valuable indicators of progression and allow study of how each participant's disease phenotype changes over time or due to treatment. Disclosure: M. Hong: None. A. Viñuela: None. R.S. Häussler: None. M. Dale: None. R.W. Koivula: None. J. Fernandez-Tajes: None. A. Mahajan: None. R. Bizzotto: Research Support; Self; GlaxoSmithKline plc. A. Mari: Consultant; Self; Eli Lilly and Company. Research Support; Self; Boehringer Ingelheim International GmbH. E. Dermitzakis: Advisory Panel; Self; DNAnexus LTD. Board Member; Self; Hybridstat LTD. M. McCarthy: Advisory Panel; Self; European Association for the Study of Diabetes, Pfizer Inc. Consultant; Self; Eli Lilly and Company, Merck & Co., Inc. Consultant; Spouse/Partner; Merck & Co., Inc. Research Support; Self; AbbVie Inc., Boehringer Ingelheim International GmbH. Research Support; Spouse/Partner; Diabetes UK. Research Support; Self; Janssen Pharmaceuticals, Inc., Merck & Co., Inc., National Institutes of Health. Research Support; Spouse/Partner; National Institutes of Health. Research Support; Self; Novo Nordisk A/S. Research Support; Spouse/Partner; Novo Nordisk A/S. Research Support; Self; Novo Nordisk Foundation, Roche Pharma, Sanofi-Aventis, Servier, Takeda Pharmaceutical Company Limited. P.W. Franks: Board Member; Self; Zoe Ltd. Research Support; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Lilly Diabetes, Novo Nordisk A/S, Novo Nordisk Foundation, Sanofi, Servier. E. Pearson: None. J.M. Schwenk: None. Funding: Innovative Medicines Initiative Joint Undertaking (115317) [ABSTRACT FROM AUTHOR]

Published
2019
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43. Processes Underlying Glycemic Deterioration in Type 2 Diabetes: An IMI DIRECT Study.

Author

Bizzotto R, Jennison C, Jones AG, Kurbasic A, Tura A, Kennedy G, Bell JD, Thomas EL, Frost G, Eriksen R, Koivula RW, Brage S, Kaye J, Hattersley AT, Heggie A, McEvoy D, 't Hart LM, Beulens JW, Elders P, Musholt PB, Ridderstråle M, Hansen TH, Allin KH, Hansen T, Vestergaard H, Lundgaard AT, Thomsen HS, De Masi F, Tsirigos KD, Brunak S, Viñuela A, Mahajan A, McDonald TJ, Kokkola T, Forgie IM, Giordano GN, Pavo I, Ruetten H, Dermitzakis E, McCarthy MI, Pedersen O, Schwenk JM, Adamski J, Franks PW, Walker M, Pearson ER, and Mari A

Subjects
Blood Glucose, Cholesterol, HDL, Humans, Insulin, Diabetes Mellitus, Type 2, Insulin Resistance, Insulin-Secreting Cells
Abstract

Objective: We investigated the processes underlying glycemic deterioration in type 2 diabetes (T2D)., Research Design and Methods: A total of 732 recently diagnosed patients with T2D from the Innovative Medicines Initiative Diabetes Research on Patient Stratification (IMI DIRECT) study were extensively phenotyped over 3 years, including measures of insulin sensitivity (OGIS), β-cell glucose sensitivity (GS), and insulin clearance (CLIm) from mixed meal tests, liver enzymes, lipid profiles, and baseline regional fat from MRI. The associations between the longitudinal metabolic patterns and HbA 1c deterioration, adjusted for changes in BMI and in diabetes medications, were assessed via stepwise multivariable linear and logistic regression., Results: Faster HbA 1c progression was independently associated with faster deterioration of OGIS and GS and increasing CLIm; visceral or liver fat, HDL-cholesterol, and triglycerides had further independent, though weaker, roles ( R 2 = 0.38). A subgroup of patients with a markedly higher progression rate (fast progressors) was clearly distinguishable considering these variables only (discrimination capacity from area under the receiver operating characteristic = 0.94). The proportion of fast progressors was reduced from 56% to 8-10% in subgroups in which only one trait among OGIS, GS, and CLIm was relatively stable (odds ratios 0.07-0.09). T2D polygenic risk score and baseline pancreatic fat, glucagon-like peptide 1, glucagon, diet, and physical activity did not show an independent role., Conclusions: Deteriorating insulin sensitivity and β-cell function, increasing insulin clearance, high visceral or liver fat, and worsening of the lipid profile are the crucial factors mediating glycemic deterioration of patients with T2D in the initial phase of the disease. Stabilization of a single trait among insulin sensitivity, β-cell function, and insulin clearance may be relevant to prevent progression., (© 2020 by the American Diabetes Association.)

Published
2021
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44. Increased insulin clearance in mice with double deletion of glucagon-like peptide-1 and glucose-dependent insulinotropic polypeptide receptors.

Author

Tura A, Bizzotto R, Yamada Y, Seino Y, Pacini G, and Ahrén B

Subjects
Animals, Blood Glucose metabolism, C-Peptide blood, Female, Gastric Inhibitory Polypeptide blood, Genotype, Glucagon-Like Peptide-1 Receptor genetics, Insulin-Secreting Cells metabolism, Kinetics, Mice, Inbred C57BL, Mice, Knockout, Models, Biological, Phenotype, Receptors, Gastrointestinal Hormone genetics, Secretory Pathway, Glucagon-Like Peptide-1 Receptor deficiency, Insulin blood, Receptors, Gastrointestinal Hormone deficiency
Abstract

To establish whether incretin hormones affect insulin clearance, the aim of this study was to assess insulin clearance in mice with genetic deletion of receptors for both glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP), so called double incretin receptor knockout mice (DIRKO). DIRKO ( n = 31) and wild-type (WT) C57BL6J mice ( n = 45) were intravenously injected with d-glucose (0.35 g/kg). Blood was sampled for 50 min and assayed for glucose, insulin, and C-peptide. Data were modeled to calculate insulin clearance; C-peptide kinetics was established after human C-peptide injection. Assessment of C-peptide kinetics revealed that C-peptide clearance was 1.66 ± 0.10 10 -3 1/min. After intravenous glucose administration, insulin clearance during first phase insulin secretion was markedly higher in DIRKO than in WT mice (0.68 ± 0.06 10 -3 l/min in DIRKO mice vs. 0.54 ± 0.03 10 -3 1/min in WT mice, P = 0.02). In contrast, there was no difference between the two groups in insulin clearance during second phase insulin secretion ( P = 0.18). In conclusion, this study evaluated C-peptide kinetics in the mouse and exploited a mathematical model to estimate insulin clearance. Results showed that DIRKO mice have higher insulin clearance than WT mice, following intravenous injection of glucose. This suggests that incretin hormones reduce insulin clearance at physiological, nonstimulated levels.

Published
2018
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45. Model Description Language (MDL): A Standard for Modeling and Simulation.

Author

Smith MK, Moodie SL, Bizzotto R, Blaudez E, Borella E, Carrara L, Chan P, Chenel M, Comets E, Gieschke R, Harling K, Harnisch L, Hartung N, Hooker AC, Karlsson MO, Kaye R, Kloft C, Kokash N, Lavielle M, Lestini G, Magni P, Mari A, Mentré F, Muselle C, Nordgren R, Nyberg HB, Parra-Guillén ZP, Pasotti L, Rode-Kristensen N, Sardu ML, Smith GR, Swat MJ, Terranova N, Yngman G, Yvon F, and Holford N

Subjects
Computer Simulation, Models, Statistical
Published
2017
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46. Glucose uptake saturation explains glucose kinetics profiles measured by different tests.

Author

Bizzotto R, Natali A, Gastaldelli A, Muscelli E, Krssak M, Brehm A, Roden M, Ferrannini E, and Mari A

Subjects
Adult, Aged, Blood Glucose metabolism, Body Mass Index, Case-Control Studies, Female, Glucose Clamp Technique, Humans, Insulin Resistance, Kinetics, Male, Middle Aged, Models, Theoretical, Diabetes Mellitus, Type 2 metabolism, Glucose metabolism, Glucose Intolerance metabolism, Hyperglycemia metabolism, Hyperinsulinism metabolism, Insulin metabolism
Abstract

It is known that for a given insulin level glucose clearance depends on glucose concentration. However, a quantitative representation of the concomitant effects of hyperinsulinemia and hyperglycemia on glucose clearance, necessary to describe heterogeneous tests such as euglycemic and hyperglycemic clamps and oral tests, is lacking. Data from five studies (123 subjects) using a glucose tracer and including all the above tests in normal and diabetic subjects were collected. A mathematical model was developed in which glucose utilization was represented as a Michaelis-Menten function of glucose with constant Km and insulin-controlled Vmax, consistently with the basic notions of glucose transport. Individual values for the model parameters were estimated using a population approach. Tracer data were accurately fitted in all tests. The estimated Km was 3.88 (2.83-5.32) mmol/l [median (interquartile range)]. Median model-derived glucose clearance at 600 pmol/l insulin was reduced from 246 to 158 ml·min(-1)·m(-2) when glucose was raised from 5 to 10 mmol/l. The model reproduced the characteristic lack of increase in glucose clearance when moderate hyperinsulinemia was accompanied by hyperglycemia. In all tests, insulin sensitivity was inversely correlated with BMI, as expected (R(2) = 0.234, P = 0.0001). In conclusion, glucose clearance in euglycemic and hyperglycemic clamps and oral tests can be described with a unifying model, consistent with the notions of glucose transport and able to reproduce the suppression of glucose clearance due to hyperglycemia observed in previous studies. The model may be important for the design of reliable glucose homeostasis simulators., (Copyright © 2016 the American Physiological Society.)

Published
2016
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