31 results on '"Bizzotto, Roberto"'
Search Results
2. Development and optimization of a fentanyl pharmacokinetic model for target-controlled infusion in anaesthetized dogs
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Cattai, Andrea, Merlanti, Roberta, Bizzotto, Roberto, Lucatello, Lorena, Capolongo, Francesca, and Franci, Paolo
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- 2023
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3. Lipid-induced glucose intolerance is driven by impaired glucose kinetics and insulin metabolism in healthy individuals
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Tricò, Domenico, Mengozzi, Alessandro, Baldi, Simona, Bizzotto, Roberto, Olaniru, Oladapo, Toczyska, Klaudia, Huang, Guo Cai, Seghieri, Marta, Frascerra, Silvia, Amiel, Stephanie A., Persaud, Shanta, Jones, Peter, Mari, Andrea, and Natali, Andrea
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- 2022
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4. The Association of Cardiometabolic, Diet and Lifestyle Parameters With Plasma Glucagon-like Peptide-1: An IMI DIRECT Study.
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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
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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]
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- 2024
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5. Dietary metabolite profiling brings new insight into the relationship between nutrition and metabolic risk: An IMI DIRECT study
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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
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- 2020
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6. A pharmacokinetic model optimized by covariates for propofol target-controlled infusion in dogs
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Cattai, Andrea, Bizzotto, Roberto, Cagnardi, Petra, Di Cesare, Federica, and Franci, Paolo
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- 2019
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7. Genetic studies of abdominal MRI data identify genes regulating hepcidin as major determinants of liver iron concentration
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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
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- 2019
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8. Sleeping oxygen saturation, rapid eye movement sleep, and the adaptation of postprandial metabolic function in insulin sensitive and resistant individuals without diabetes
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Garcia, Karin A., Wohlgemuth, William K., Ferrannini, Ele, Mari, Andrea, Gonzalez, Alex, Mendez, Armando J., Bizzotto, Roberto, Skyler, Jay S., Schneiderman, Neil, and Hurwitz, Barry E.
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- 2018
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9. Inhibition of sweet chemosensory receptors alters insulin responses during glucose ingestion in healthy adults: a randomized crossover interventional study
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Karimian Azari, Elnaz, Smith, Kathleen R, Yi, Fanchao, Osborne, Timothy F, Bizzotto, Roberto, Mari, Andrea, Pratley, Richard E, and Kyriazis, George A
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- 2017
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10. At any Level of Adiposity, Relatively Elevated Leptin Concentrations Are Associated With Decreased Insulin Sensitivity.
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Chiriacò, Martina, Nesti, Lorenzo, Flyvbjerg, Allan, Golay, Alain, Nazare, Julie-Anne, Anderwald, Christian-Heinz, Mitrakou, Asimina, Bizzotto, Roberto, Mari, Andrea, and Natali, Andrea
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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/m
2 ) 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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11. Discovery of drug–omics associations in type 2 diabetes with generative deep-learning models
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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, S. ren, Froguel, Philippe, Thomas, Cecilia Engel, Haussler, Ragna, 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, van Leeuwen, Nienke, Groop, Leif, Slieker, Roderick, Ramisch, Anna, Jennison, Christopher, McVittie, Ian, Frau, Francesca, Steckel-Hamann, Birgit, Adragni, Kofi, Thomas, Melissa, Pasdar, Naeimeh Atabaki, Fitipaldi, Hugo, Kurbasic, Azra, Mutie, Pascal, Pomares-Millan, Hugo, Bonnefond, Amelie, Canouil, Mickael, Caiazzo, Robert, Verkindt, Helene, Holl, Reinhard, Kuulasmaa, Teemu, Deshmukh, Harshal, Cederberg, Henna, Laakso, Markku, Vangipurapu, Jagadish, Dale, Matilda, Thorand, Barbara, Nicolay, Claudia, Fritsche, Andreas, Hill, Anita, Hudson, Michelle, Thorne, Claire, Allin, Kristine, Arumugam, Manimozhiyan, Jonsson, Anna, Engelbrechtsen, Line, Forman, Annemette, Dutta, Avirup, Sondertoft, Nadja, Fan, Yong, Gough, Stephen, Robertson, Neil, McRobert, Nicky, Wesolowska-Andersen, Agata, Brown, Andrew, Davtian, David, Dawed, Adem, Donnelly, Louise, Palmer, Colin, White, Margaret, Ferrer, Jorge, Whitcher, Brandon, Artati, Anna, Prehn, Cornelia, Adam, Jonathan, Grallert, Harald, Gupta, Ramneek, Sackett, Peter Wad, Nilsson, Birgitte, Tsirigos, Konstantinos, Eriksen, Rebeca, Jablonka, Bernd, Uhlen, Mathias, Gassenhuber, Johann, Baltauss, Tania, de Preville, Nathalie, Klintenberg, Maria, Abdalla, Moustafa, Lundgaard, Agnete Troen [0000-0001-7447-6560], Hernández Medina, Ricardo [0000-0001-6373-2362], Johansen, Joachim [0000-0001-7052-1870], Niu, Lili [0000-0003-4571-4368], Biel, Jorge Hernansanz [0000-0002-3125-2951], Benros, Michael Eriksen [0000-0003-4939-9465], Pedersen, Anders Gorm [0000-0001-9650-8965], Jacobsen, Ulrik Plesner [0000-0001-9181-6854], Koivula, Robert [0000-0002-1646-4163], Vinuela, Ana [0000-0003-3771-8537], Haid, Mark [0000-0001-6118-1333], Hong, Mun-Gwan [0000-0001-8603-8293], Kennedy, Gwen [0000-0002-9856-3236], Thomas, E Louise [0000-0003-4235-4694], Frost, Gary [0000-0003-0529-6325], Hansen, Tue Haldor [0000-0001-5948-8993], Kaye, Jane [0000-0002-7311-4725], Hattersley, Andrew [0000-0001-5620-473X], Ridderstråle, Martin [0000-0002-3270-9167], Pedersen, Oluf [0000-0002-3321-3972], Hansen, Torben [0000-0001-8748-3831], Schwenk, Jochen M [0000-0001-8141-8449], Rasmussen, Simon [0000-0001-6323-9041], Brunak, Søren [0000-0003-0316-5866], Apollo - University of Cambridge Repository, Epidemiology and Data Science, ACS - Diabetes & metabolism, APH - Health Behaviors & Chronic Diseases, General practice, ACS - Heart failure & arrhythmias, APH - Aging & Later Life, Graduate School, and APH - Methodology
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Biomedical Engineering ,Type 2 diabetes ,Bioengineering ,Applied Microbiology and Biotechnology ,Deep Learning ,SDG 3 - Good Health and Well-being ,Diabetes Mellitus, Type 2 ,Machine learning ,Molecular Medicine ,Humans ,Data integration ,IMI DIRECT Consortium ,Systems biology ,Algorithms ,Biotechnology - 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.
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- 2023
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12. Adaptation of β-Cell and Endothelial Function to Carbohydrate Loading: Influence of Insulin Resistance
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Hurwitz, Barry E., Schneiderman, Neil, Marks, Jennifer B., Mendez, Armando J., Gonzalez, Alex, Llabre, Maria M., Smith, Steven R., Bizzotto, Roberto, Santini, Eleonora, Manca, Maria Laura, Skyler, Jay S., Mari, Andrea, and Ferrannini, Ele
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- 2015
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13. Incretin and Islet Hormone Responses to Meals of Increasing Size in Healthy Subjects
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Alsalim, Wathik, Omar, Bilal, Pacini, Giovanni, Bizzotto, Roberto, Mari, Andrea, and Ahrén, Bo
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- 2015
14. Multinomial Logistic Functions in Markov Chain Models of Sleep Architecture: Internal and External Validation and Covariate Analysis
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Bizzotto, Roberto, Zamuner, Stefano, Mezzalana, Enrica, De Nicolao, Giuseppe, Gomeni, Roberto, Hooker, Andrew C., and Karlsson, Mats O.
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- 2011
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15. Multinomial logistic estimation of Markov-chain models for modeling sleep architecture in primary insomnia patients
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Bizzotto, Roberto, Zamuner, Stefano, De Nicolao, Giuseppe, Karlsson, Mats O., and Gomeni, Roberto
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- 2010
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16. Genetic studies of abdominal MRI data identify genes regulating hepcidin as major determinants of liver iron concentration
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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
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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
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- 2019
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17. New Insights on the Interactions Between Insulin Clearance and the Main Glucose Homeostasis Mechanisms.
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Bizzotto, Roberto, Tricò, Domenico, Natali, Andrea, Gastaldelli, Amalia, Muscelli, Elza, De Fronzo, Ralph A., Arslanian, Silva, Ferrannini, Ele, and Mari, Andrea
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INSULIN , *GLUCOSE , *INSULIN resistance , *GLUCOSE tolerance tests , *TYPE 2 diabetes - Abstract
Objective: Endogenous insulin clearance (EIC) is physiologically reduced at increasing insulin secretion rate (ISR). Computing EIC at the prevailing ISR does not distinguish the effects of hypersecretion from those of other mechanisms of glucose homeostasis. We aimed to measure EIC in standardized ISR conditions (i.e., at fixed ISR levels) and to analyze its associations with relevant physiologic factors.Research Design and Methods: We estimated standardized EIC (EICISR) by mathematical modeling in nine different studies with insulin and glucose infusions (N = 2,067). EICISR association with various traits was analyzed by stepwise multivariable regression in studies with both euglycemic clamp and oral glucose tolerance test (OGTT) (N = 1,410). We also tested whether oral glucose ingestion, as opposed to intravenous infusion, has an independent effect on EIC (N = 1,555).Results: Insulin sensitivity (as M/I from the euglycemic clamp) is the strongest determinant of EICISR, approximately four times more influential than insulin resistance-related hypersecretion. EICISR independently associates positively with M/I, fasting and mean OGTT glucose or type 2 diabetes, and β-cell glucose sensitivity and negatively with African American or Hispanic race, female sex, and female age. With oral glucose ingestion, an ISR-independent ∼10% EIC reduction is necessary to explain the observed insulin concentration profiles.Conclusions: Based on EICISR, we posit the existence of two adaptive processes involving insulin clearance: the first reduces EICISR with insulin resistance (not with higher BMI per se) and is more relevant than the concomitant hypersecretion; the second reduces EICISR with β-cell dysfunction. These processes are dysregulated in type 2 diabetes. Finally, oral glucose ingestion per se reduces insulin clearance. [ABSTRACT FROM AUTHOR]- Published
- 2021
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18. Processes Underlying Glycemic Deterioration in Type 2 Diabetes: An IMI DIRECT Study.
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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, and Musholt, Petra B.
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TYPE 2 diabetes , *GLUCAGON-like peptide 1 , *INSULIN sensitivity , *RECEIVER operating characteristic curves , *GLUCAGON-like peptides , *LIVER enzymes - 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 HbA1c deterioration, adjusted for changes in BMI and in diabetes medications, were assessed via stepwise multivariable linear and logistic regression.Results: Faster HbA1c 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 (R2 = 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. [ABSTRACT FROM AUTHOR]- Published
- 2021
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19. Mathematical Modeling for the Physiological and Clinical Investigation of Glucose Homeostasis and Diabetes.
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Mari, Andrea, Tura, Andrea, Grespan, Eleonora, and Bizzotto, Roberto
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TYPE 2 diabetes ,PHYSIOLOGICAL models ,GLUCOSE ,HOMEOSTASIS ,MATHEMATICAL models - Abstract
Mathematical modeling in the field of glucose metabolism has a longstanding tradition. The use of models is motivated by several reasons. Models have been used for calculating parameters of physiological interest from experimental data indirectly, to provide an unambiguous quantitative representation of pathophysiological mechanisms, to determine indices of clinical usefulness from simple experimental tests. With the growing societal impact of type 2 diabetes, which involves the disturbance of the glucose homeostasis system, development and use of models in this area have increased. Following the approaches of physiological and clinical investigation, the focus of the models has spanned from representations of whole body processes to those of cells, i.e., from in vivo to in vitro research. Model-based approaches for linking in vivo to in vitro research have been proposed, as well as multiscale models merging the two areas. The success and impact of models has been variable. Two kinds of models have received remarkable interest: those widely used in clinical applications, e.g., for the assessment of insulin sensitivity and β-cell function and some models representing specific aspects of the glucose homeostasis system, which have become iconic for their efficacy in describing clearly and compactly key physiological processes, such as insulin secretion from the pancreatic β cells. Models are inevitably simplified and approximate representations of a physiological system. Key to their success is an appropriate balance between adherence to reality, comprehensibility, interpretative value and practical usefulness. This has been achieved with a variety of approaches. Although many models concerning the glucose homeostasis system have been proposed, research in this area still needs to address numerous issues and tackle new opportunities. The mathematical representation of the glucose homeostasis processes is only partial, also because some mechanisms are still only partially understood. For in vitro research, mathematical models still need to develop their potential. This review illustrates the problems, approaches and contribution of mathematical modeling to the physiological and clinical investigation of glucose homeostasis and diabetes, focusing on the most relevant and stimulating models. [ABSTRACT FROM AUTHOR]
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- 2020
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20. Increased insulin clearance in mice with double deletion of glucagon-like peptide-1 and glucose-dependent insulinotropic polypeptide receptors.
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Tura, Andrea, Bizzotto, Roberto, Yuchiro Yamada, Yutaka Seino, Pacini, Giovanni, and Ahrén, Bo
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GASTRIC inhibitory polypeptide , *INSULIN , *C-peptide - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
21. Analysis of variability in length of sleep state bouts reveals memory-free sleep subcomponents consistent among primary insomnia patients.
- Author
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Bizzotto, Roberto and Zamuner, Stefano
- Subjects
- *
INSOMNIACS , *RAPID eye movement sleep , *DISTRIBUTION (Probability theory) , *SLEEP , *MARKOV processes , *MULTISENSOR data fusion - Abstract
The statistical distributions of bout lengths for the different (macro) sleep states (wake, N1, N2, N3, and REM sleep) are essential to understanding whether any memory-free subcomponent ("micro state") is involved in the organization of sleep. Micro state detection can be prevented by the fusion of data including various sources of variability, in particular by the differences in sleep architecture between individuals, along sleep time (or nighttime), or between different nights. In this analysis, a mathematical model of sleep was adopted to disentangle these features and advance the understanding of the dynamics and mechanisms of sleep and its states. The analysis involved 116 primary insomnia patients taking placebo before going to bed and undergoing polysomnography for one night. The individual sequences of macro sleep states had been previously modeled with a mixedeffect nonhomogeneous modified Markov chain model, from which individual conditional probability distributions for the bout durations were derived in this analysis as functions of sleep time. The probability distributions, affected by neither subject, night-time, nor multiple-night pooling, substantially changed at ¼ and ¾ sleep time, had modified exponential shape, and were best described as the sum of one to four exponentials, depending on the sleep state. The time constants and proportions of bouts contributing to each exponential were similar in the different subjects, changing over sleep time. Variability in bout durations thus indicated the presence of multiple memory-free sleep subcomponents whose mean residence times and access probabilities could be identified and shown to be consistent among the studied subjects. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
22. Effect of single‐dose DPP‐4 inhibitor sitagliptin on β‐cell function and incretin hormone secretion after meal ingestion in healthy volunteers and drug‐naïve, well‐controlled type 2 diabetes subjects.
- Author
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Alsalim, Wathik, Ahrén, Bo, Göransson, Olga, Carr, Richard D., Bizzotto, Roberto, Tura, Andrea, Pacini, Giovanni, and Mari, Andrea
- Subjects
SITAGLIPTIN ,TYPE 2 diabetes treatment ,PANCREATIC beta cells ,GLUCAGON-like peptide 1 ,CD26 antigen - Abstract
To explore the effects of a single dose of the DPP‐4 inhibitor sitagliptin on glucose‐standardized insulin secretion and β‐cell glucose sensitivity after meal ingestion, 12 healthy and 12 drug‐naïve, well‐controlled type 2 diabetes (T2D) subjects (mean HbA1c 43 mmol/mol, 6.2%) received sitagliptin (100 mg) or placebo before a meal (525 kcal). β‐cell function was measured as the insulin secretory rate at a standardized glucose concentration and the β‐cell glucose sensitivity (the slope between glucose and insulin secretory rate). Incretin levels were also monitored. Sitagliptin increased standardized insulin secretion, in both healthy and T2D subjects, compared to placebo, but without increasing β‐cell glucose sensitivity. Sitagliptin also increased active glucose‐dependent insulinotropic polypeptide (GIP) and glucagon‐like peptide‐1 (GLP‐1) and reduced total (reflecting the secretion) GIP, but not total GLP‐1 levels. We conclude that a single dose of DPP‐4 inhibition induces dissociated effects on different aspects of β‐cell function and incretin hormones after meal ingestion in both healthy and well‐controlled T2D subjects. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
23. A Mixed-Effect Multinomial Markov-Chain Model for Describing Sleep Architecture in Insomniac Patients
- Author
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Bizzotto, Roberto
- Subjects
Settore ING-INF/06 - Bioingegneria Elettronica e Informatica ,categorical sleep PKPD NONMEM logistic multinomial polychotomous evaluation validation VEC VPC covariate - Published
- 2011
24. GLP-1 response to sequential mixed meals: influence of insulin resistance.
- Author
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Rebelos, Eleni, Astiarraga, Brenno, Bizzotto, Roberto, Mari, Andrea, Manca, Maria Laura, Gonzalez, Alex, Mendez, Armando, Martinez, Claudia A., Hurwitz, Barry E., and Ferrannini, Ele
- Subjects
GLUCAGON-like peptide 1 ,INSULIN resistance ,HIGH-carbohydrate diet ,GLUCOSE clamp technique ,FATTY acids - Abstract
Previous work has shown that potentiation of insulin release is impaired in non-diabetic insulin resistance; we tested the hypothesis that this defect may be related to altered glucagon-like peptide-1 (GLP-1) release. On consecutive days, 82 non-diabetic individuals, classified as insulin sensitive (IS, n=41) or insulin resistant (IR, n=41) by the euglycaemic clamp, were given two sequential mixed meals with standard (75 g, LCD) or double (150 g, HCD) carbohydrate content. Plasma glucose, insulin, C-peptide, non-esterified fatty acids (NEFA) and GLP-1 concentrations were measured; β-cell function (glucose sensitivity and potentiation) was resolved by mathematical modelling. Fasting GLP-1 levels were higher in IR than IS (by 15%, P=0.006), and reciprocally related to insulin sensitivity after adjustment for sex, age, fat mass, fasting glucose or insulin concentrations. Mean postprandial GLP-1 responses were tightly correlated with fasting GLP-1, were higher for the second than the first meal, and higher in IR than IS subjects but only with LCD. In contrast, incremental GLP-1 responses were higher during (i) the second than the first meal, (ii) on HCD than LCD, and (iii) significantly smaller in IR than IS independently of meal and load. Potentiation of insulin release was markedly reduced in IR vs IS across meal and carbohydrate loading. In the whole dataset, incremental GLP-1 was directly related to potentiation, and both were inversely related to mean NEFA concentrations. We conclude that (a) raised GLP-1 tone may be inherently linked with a reduced GLP-1 response and (b) defective post-meal GLP-1 response may be one mechanism for impaired potentiation of insulin release in insulin resistance. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
25. Model Description Language (MDL): A Standard for Modeling and Simulation.
- Author
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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, and Lestini, Giulia
- Subjects
PHARMACOLOGY ,PROGRAMMING languages ,DATA analysis ,KNOWLEDGE management ,SIMULATION methods & models - Published
- 2017
- Full Text
- View/download PDF
26. Inhibition of sweet chemosensory receptors alters insulin responses during glucose ingestion in healthy adults: a randomized crossover interventional study.
- Author
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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/m
2 ) 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
- Full Text
- View/download PDF
27. Glucose uptake saturation explains glucose kinetics profiles measured by different tests.
- Author
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Bizzotto, Roberto, Natali, Andrea, Gastaldelli, Amalia, Muscelli, Elza, Krssak, Martin, Brehm, Attila, Roden, Michael, Ferrannini, Ele, and Mari, Andrea
- Subjects
- *
GLUCOSE in the body , *HYPERINSULINISM , *HYPERGLYCEMIA , *INSULIN resistance , *HOMEOSTASIS - 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 (R² = 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. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
28. Shift to Fatty Substrate Utilization in Response to Sodium-Glucose Cotransporter 2 Inhibition in Subjects Without Diabetes and Patients With Type 2 Diabetes.
- Author
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Ferrannini, Ele, Baldi, Simona, Frascerra, Silvia, Astiarraga, Brenno, Heise, Tim, Bizzotto, Roberto, Mari, Andrea, Pieber, Thomas R., and Muscelli, Elza
- Subjects
GLYCOSURIA ,HOMEOSTASIS ,TYPE 2 diabetes ,SODIUM cotransport systems ,LIPIDS ,DRUG side effects - Abstract
Pharmacologically induced glycosuria elicits adaptive responses in glucose homeostasis and hormone release. In type 2 diabetes (T2D), along with decrements in plasma glucose and insulin levels and increments in glucagon release, sodium-glucose cotransporter 2 (SGLT2) inhibitors induce stimulation of endogenous glucose production (EGP) and a suppression of tissue glucose disposal (TGD). We measured fasting and postmeal glucose fluxes in 25 subjects without diabetes using a double glucose tracer technique; in these subjects and in 66 previously reported patients with T2D, we also estimated lipolysis (from [(2)H5]glycerol turnover rate and circulating free fatty acids, glycerol, and triglycerides), lipid oxidation (LOx; by indirect calorimetry), and ketogenesis (from circulating β-hydroxybutyrate concentrations). In both groups, empagliflozin administration raised EGP, lowered TGD, and stimulated lipolysis, LOx, and ketogenesis. The pattern of glycosuria-induced changes was similar in subjects without diabetes and in those with T2D but quantitatively smaller in the former. With chronic (4 weeks) versus acute (first dose) drug administration, glucose flux responses were attenuated, whereas lipid responses were enhanced; in patients with T2D, fasting β-hydroxybutyrate levels rose from 246 ± 288 to 561 ± 596 µmol/L (P < 0.01). We conclude that by shunting substantial amounts of carbohydrate into urine, SGLT2-mediated glycosuria results in a progressive shift in fuel utilization toward fatty substrates. The associated hormonal milieu (lower insulin-to-glucagon ratio) favors glucose release and ketogenesis. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
29. 189-OR: Plasma Proteome Profiling of Prediabetes and Diabetes Progression: An IMI Direct Study.
- Author
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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 (HbA
1c ~ 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
- Full Text
- View/download PDF
30. Multinomial Markov-chain model of sleep architecture in Phase Advanced Subjects
- Author
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Ernest II, Charles, Bizzotto, Roberto, DeBrota, David J., Ni, Lan, Harris, Cynthia J., Karlsson, Mats O., and Hooker, Andrew C.
- Subjects
Pharmaceutical Sciences ,Farmaceutiska vetenskaper
31. Four groups of type 2 diabetes contribute to the etiological and clinical heterogeneity in newly diagnosed individuals: An IMI DIRECT study.
- Author
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Wesolowska-Andersen A, Brorsson CA, Bizzotto R, Mari A, Tura A, Koivula R, Mahajan A, Vinuela A, Tajes JF, Sharma S, Haid M, Prehn C, Artati A, Hong MG, Musholt PB, Kurbasic A, De Masi F, Tsirigos K, Pedersen HK, Gudmundsdottir V, Thomas CE, Banasik K, Jennison C, Jones A, Kennedy G, Bell J, Thomas L, Frost G, Thomsen H, Allin K, Hansen TH, Vestergaard H, Hansen T, Rutters F, Elders P, t'Hart L, Bonnefond A, Canouil M, Brage S, Kokkola T, Heggie A, McEvoy D, Hattersley A, McDonald T, Teare H, Ridderstrale M, Walker M, Forgie I, Giordano GN, Froguel P, Pavo I, Ruetten H, Pedersen O, Dermitzakis E, Franks PW, Schwenk JM, Adamski J, Pearson E, McCarthy MI, and Brunak S
- Subjects
- Adult, Diabetes Mellitus, Type 2 genetics, Disease Progression, Female, Follow-Up Studies, Genetic Predisposition to Disease, Genomics, Humans, Male, Middle Aged, Phenotype, Risk Factors, Diabetes Mellitus, Type 2 diagnosis
- Abstract
The presentation and underlying pathophysiology of type 2 diabetes (T2D) is complex and heterogeneous. Recent studies attempted to stratify T2D into distinct subgroups using data-driven approaches, but their clinical utility may be limited if categorical representations of complex phenotypes are suboptimal. We apply a soft-clustering (archetype) method to characterize newly diagnosed T2D based on 32 clinical variables. We assign quantitative clustering scores for individuals and investigate the associations with glycemic deterioration, genetic risk scores, circulating omics biomarkers, and phenotypic stability over 36 months. Four archetype profiles represent dysfunction patterns across combinations of T2D etiological processes and correlate with multiple circulating biomarkers. One archetype associated with obesity, insulin resistance, dyslipidemia, and impaired β cell glucose sensitivity corresponds with the fastest disease progression and highest demand for anti-diabetic treatment. We demonstrate that clinical heterogeneity in T2D can be mapped to heterogeneity in individual etiological processes, providing a potential route to personalized treatments., Competing Interests: The views expressed in this article are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health. M.I.C. has served on advisory panels for Pfizer, Novo Nordisk, and Zoe Global; has received honoraria from Merck, Pfizer, Novo Nordisk, and Eli Lilly; and has received research funding from Abbvie, Astra Zeneca, Boehringer Ingelheim, Eli Lilly, Janssen, Merck, Novo Nordisk, Pfizer, Roche, Sanofi Aventis, Servier, and Takeda. As of June 2019, M.I.C. is an employee of Genentech and a holder of Roche stock. S.B. is holder of stock in Intomics, Hoba Therapeutics, Novo Nordisk, and Lundbeck and holds managing board memberships in Proscion and Intomics., (© 2021 The Authors.)
- Published
- 2022
- Full Text
- View/download PDF
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