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6. Identification of biomarkers for glycaemic deterioration in type 2 diabetes

Author

Slieker, Roderick C., Donnelly, Louise A., Akalestou, Elina, Lopez-Noriega, Livia, Melhem, Rana, Güneş, Ayşim, Abou Azar, Frederic, Efanov, Alexander, Georgiadou, Eleni, Muniangi-Muhitu, Hermine, Sheikh, Mahsa, Giordano, Giuseppe N., Åkerlund, Mikael, Ahlqvist, Emma, Ali, Ashfaq, Banasik, Karina, Brunak, Søren, Barovic, Marko, Bouland, Gerard A., Burdet, Frédéric, Canouil, Mickaël, Dragan, Iulian, Elders, Petra J. M., Fernandez, Celine, Festa, Andreas, Fitipaldi, Hugo, Froguel, Phillippe, Gudmundsdottir, Valborg, Gudnason, Vilmundur, Gerl, Mathias J., van der Heijden, Amber A., Jennings, Lori L., Hansen, Michael K., Kim, Min, Leclerc, Isabelle, Klose, Christian, Kuznetsov, Dmitry, Mansour Aly, Dina, Mehl, Florence, Marek, Diana, Melander, Olle, Niknejad, Anne, Ottosson, Filip, Pavo, Imre, Duffin, Kevin, Syed, Samreen K., Shaw, Janice L., Cabrera, Over, Pullen, Timothy J., Simons, Kai, Solimena, Michele, Suvitaival, Tommi, Wretlind, Asger, Rossing, Peter, Lyssenko, Valeriya, Legido Quigley, Cristina, Groop, Leif, Thorens, Bernard, Franks, Paul W., Lim, Gareth E., Estall, Jennifer, Ibberson, Mark, Beulens, Joline W. J., ’t Hart, Leen M, Pearson, Ewan R., and Rutter, Guy A.

Published
2023
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8. The Effect of Metformin Treatment on the Circulating Proteome

Author

Connolly, Benjamin W, primary, McCreight, Laura, additional, Slieker, Roderick, additional, Bedair, Khaled F, additional, Donnelly, Louise, additional, de Klerk, Juliette A, additional, Beulens, Joline WJ, additional, Elders, Petra M, additional, Bergstrom, Goran, additional, Hong, Mun-Gwan, additional, Koivula, Robert w, additional, Franks, Paul W, additional, 't Hart, Leen, additional, Schwenk, Jochen, additional, Gummesson, Anders, additional, and Pearson, Ewan R, additional

Published
2024
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13. Multi-omics subgroups associated with glycaemic deterioration in type 2 diabetes: an IMI-RHAPSODY Study

Author

Li, Shiying, primary, Dragan, Iulian, additional, Tran, Van Du T., additional, Fung, Chun Ho, additional, Kuznetsov, Dmitry, additional, Hansen, Michael K., additional, Beulens, Joline W. J., additional, Hart, Leen M. ‘t, additional, Slieker, Roderick C., additional, Donnelly, Louise A., additional, Gerl, Mathias J., additional, Klose, Christian, additional, Mehl, Florence, additional, Simons, Kai, additional, Elders, Petra J. M., additional, Pearson, Ewan R., additional, Rutter, Guy A., additional, and Ibberson, Mark, additional

Published
2024
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14. Replication and cross-validation of type 2 diabetes subtypes based on clinical variables: an IMI-RHAPSODY study

Author

Slieker, Roderick C., Donnelly, Louise A., Fitipaldi, Hugo, Bouland, Gerard A., Giordano, Giuseppe N., Åkerlund, Mikael, Gerl, Mathias J., Ahlqvist, Emma, Ali, Ashfaq, Dragan, Iulian, Festa, Andreas, Hansen, Michael K., Mansour Aly, Dina, Kim, Min, Kuznetsov, Dmitry, Mehl, Florence, Klose, Christian, Simons, Kai, Pavo, Imre, Pullen, Timothy J., Suvitaival, Tommi, Wretlind, Asger, Rossing, Peter, Lyssenko, Valeriya, Legido-Quigley, Cristina, Groop, Leif, Thorens, Bernard, Franks, Paul W., Ibberson, Mark, Rutter, Guy A., Beulens, Joline W. J., ‘t Hart, Leen M., and Pearson, Ewan R.

Published
2021
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15. Inhaled budesonide in the treatment of early COVID-19 (STOIC): a phase 2, open-label, randomised controlled trial

Author

Ramakrishnan, Sanjay, Nicolau, Dan V, Jr, Langford, Beverly, Mahdi, Mahdi, Jeffers, Helen, Mwasuku, Christine, Krassowska, Karolina, Fox, Robin, Binnian, Ian, Glover, Victoria, Bright, Stephen, Butler, Christopher, Cane, Jennifer L, Halner, Andreas, Matthews, Philippa C, Donnelly, Louise E, Simpson, Jodie L, Baker, Jonathan R, Fadai, Nabil T, Peterson, Stefan, Bengtsson, Thomas, Barnes, Peter J, Russell, Richard E K, and Bafadhel, Mona

Published
2021
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17. An omics-based machine learning approach to predict diabetes progression: a RHAPSODY study

Author

Circulatory Health, JC onderzoeksprogramma Cardiovasculaire Epidemiologie, Cardiometabolic Health, Slieker, Roderick C., Münch, Magnus, Donnelly, Louise A., Bouland, Gerard A., Dragan, Iulian, Kuznetsov, Dmitry, Elders, Petra J.M., Rutter, Guy A., Ibberson, Mark, Pearson, Ewan R., ’t Hart, Leen M., van de Wiel, Mark A., Beulens, Joline W.J., Circulatory Health, JC onderzoeksprogramma Cardiovasculaire Epidemiologie, Cardiometabolic Health, Slieker, Roderick C., Münch, Magnus, Donnelly, Louise A., Bouland, Gerard A., Dragan, Iulian, Kuznetsov, Dmitry, Elders, Petra J.M., Rutter, Guy A., Ibberson, Mark, Pearson, Ewan R., ’t Hart, Leen M., van de Wiel, Mark A., and Beulens, Joline W.J.

Published
2024

18. Trajectories of clinical characteristics, complications and treatment choices in data-driven subgroups of type 2 diabetes

Author

Circulatory Health, JC onderzoeksprogramma Cardiovasculaire Epidemiologie, Cardiometabolic Health, Li, Xinyu, Donnelly, Louise A., Slieker, Roderick C., Beulens, Joline W.J., ‘t Hart, Leen M., Elders, Petra J.M., Pearson, Ewan R., van Giessen, Anoukh, Leal, Jose, Feenstra, Talitha, Circulatory Health, JC onderzoeksprogramma Cardiovasculaire Epidemiologie, Cardiometabolic Health, Li, Xinyu, Donnelly, Louise A., Slieker, Roderick C., Beulens, Joline W.J., ‘t Hart, Leen M., Elders, Petra J.M., Pearson, Ewan R., van Giessen, Anoukh, Leal, Jose, and Feenstra, Talitha

Published
2024

19. Coordinated nasal mucosa-mediated immunity accelerates recovery from COVID-19

Author

Cass, Steven P., Nicolau, Dan V., Baker, Jonathan R., Mwasuku, Christine, Ramakrishnan, Sanjay, Mahdi, Mahdi, Barnes, Peter J., Donnelly, Louise E., Martinez-Nunez, Rocio T., Russell, Richard E.K., Bafadhel, Mona, Cass, Steven P., Nicolau, Dan V., Baker, Jonathan R., Mwasuku, Christine, Ramakrishnan, Sanjay, Mahdi, Mahdi, Barnes, Peter J., Donnelly, Louise E., Martinez-Nunez, Rocio T., Russell, Richard E.K., and Bafadhel, Mona

Abstract

Introduction Understanding the interplay of immune mediators in relation to clinical outcomes during acute infection has the potential to highlight immune networks critical to symptom recovery. The objective of the present study was to elucidate the immune networks critical to early symptom resolution following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Methods In a community-based randomised clinical trial comparing inhaled budesonide against usual care in 139 participants with early onset SARS-CoV-2 (the STOIC study; clinicaltrials.gov identifier NCT04416399), significant clinical deterioration (reported need for urgent care, emergency department visit, hospitalisation: the primary outcome), self-reported symptom severity (Influenza Patient-Reported Outcome questionnaire) and immune mediator networks were assessed. Immune mediator networks were determined using pre-defined mathematical modelling of immune mediators, determined by the Meso Scale Discovery U-Plex platform, within the first 7 days of SARS-CoV-2 infection compared to 22 healthy controls. Results Interferon-and chemokine-dominant networks were associated with high viral burden. Elevated levels of the mucosal network (chemokine (C-C motif) ligand (CCL)13, CCL17, interleukin (IL)-33, IL-5, IL-4, CCL26, IL-2, IL-12 and granulocyte–macrophage colony-stimulating factor) was associated with a mean 3.7-day quicker recovery time, with no primary outcome events, irrespective of treatment arm. This mucosal network was associated with initial nasal and throat symptoms at day 0. Conclusion A nasal immune network is critical to accelerated recovery and improved patient outcomes in community-acquired viral infections. Overall, early prognostication and treatments aimed at inducing epithelial responses may prove clinically beneficial in enhancing early host response to virus.

Published
2024

20. An omics-based machine learning approach to predict diabetes progression: a RHAPSODY study

Author

Slieker, Roderick C. (author), Münch, Magnus (author), Donnelly, Louise A. (author), Bouland, G.A. (author), Dragan, Iulian (author), Kuznetsov, Dmitry (author), Elders, Petra J.M. (author), Rutter, Guy A. (author), Ibberson, Mark (author), Slieker, Roderick C. (author), Münch, Magnus (author), Donnelly, Louise A. (author), Bouland, G.A. (author), Dragan, Iulian (author), Kuznetsov, Dmitry (author), Elders, Petra J.M. (author), Rutter, Guy A. (author), and Ibberson, Mark (author)

Abstract

Aims/hypothesis: People with type 2 diabetes are heterogeneous in their disease trajectory, with some progressing more quickly to insulin initiation than others. Although classical biomarkers such as age, HbA1c and diabetes duration are associated with glycaemic progression, it is unclear how well such variables predict insulin initiation or requirement and whether newly identified markers have added predictive value. Methods: In two prospective cohort studies as part of IMI-RHAPSODY, we investigated whether clinical variables and three types of molecular markers (metabolites, lipids, proteins) can predict time to insulin requirement using different machine learning approaches (lasso, ridge, GRridge, random forest). Clinical variables included age, sex, HbA1c, HDL-cholesterol and C-peptide. Models were run with unpenalised clinical variables (i.e. always included in the model without weights) or penalised clinical variables, or without clinical variables. Model development was performed in one cohort and the model was applied in a second cohort. Model performance was evaluated using Harrel’s C statistic. Results: Of the 585 individuals from the Hoorn Diabetes Care System (DCS) cohort, 69 required insulin during follow-up (1.0–11.4 years); of the 571 individuals in the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) cohort, 175 required insulin during follow-up (0.3–11.8 years). Overall, the clinical variables and proteins were selected in the different models most often, followed by the metabolites. The most frequently selected clinical variables were HbA1c (18 of the 36 models, 50%), age (15 models, 41.2%) and C-peptide (15 models, 41.2%). Base models (age, sex, BMI, HbA1c) including only clinical variables performed moderately in both the DCS discovery cohort (C statistic 0.71 [95% CI 0.64, 0.79]) and the GoDARTS replication cohort (C 0.71 [95% CI 0.69, 0.75]). A more extensive model including HD, Pattern Recognition and Bioinformatics

Published
2024
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22. Coordinated nasal mucosa-mediated immunity accelerates recovery from COVID-19

Author

Cass, Steven P., primary, Nicolau, Dan V., additional, Baker, Jonathan R., additional, Mwasuku, Christine, additional, Ramakrishnan, Sanjay, additional, Mahdi, Mahdi, additional, Barnes, Peter J., additional, Donnelly, Louise E., additional, Martinez-Nunez, Rocio T., additional, Russell, Richard E. K., additional, and Bafadhel, Mona, additional

Published
2024
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23. The genetics of blood pressure regulation and its target organs from association studies in 342,415 individuals

Author

Ehret, Georg B, Ferreira, Teresa, Chasman, Daniel I, Jackson, Anne U, Schmidt, Ellen M, Johnson, Toby, Thorleifsson, Gudmar, Luan, Jian'an, Donnelly, Louise A, Kanoni, Stavroula, Petersen, Ann-Kristin, Pihur, Vasyl, Strawbridge, Rona J, Shungin, Dmitry, Hughes, Maria F, Meirelles, Osorio, Kaakinen, Marika, Bouatia-Naji, Nabila, Kristiansson, Kati, Shah, Sonia, Kleber, Marcus E, Guo, Xiuqing, Lyytikäinen, Leo-Pekka, Fava, Cristiano, Eriksson, Niclas, Nolte, Ilja M, Magnusson, Patrik K, Salfati, Elias L, Rallidis, Loukianos S, Theusch, Elizabeth, Smith, Andrew JP, Folkersen, Lasse, Witkowska, Kate, Pers, Tune H, Joehanes, Roby, Kim, Stuart K, Lataniotis, Lazaros, Jansen, Rick, Johnson, Andrew D, Warren, Helen, Kim, Young Jin, Zhao, Wei, Wu, Ying, Tayo, Bamidele O, Bochud, Murielle, Absher, Devin, Adair, Linda S, Amin, Najaf, Arking, Dan E, Axelsson, Tomas, Baldassarre, Damiano, Balkau, Beverley, Bandinelli, Stefania, Barnes, Michael R, Barroso, Inês, Bevan, Stephen, Bis, Joshua C, Bjornsdottir, Gyda, Boehnke, Michael, Boerwinkle, Eric, Bonnycastle, Lori L, Boomsma, Dorret I, Bornstein, Stefan R, Brown, Morris J, Burnier, Michel, Cabrera, Claudia P, Chambers, John C, Chang, I-Shou, Cheng, Ching-Yu, Chines, Peter S, Chung, Ren-Hua, Collins, Francis S, Connell, John M, Döring, Angela, Dallongeville, Jean, Danesh, John, de Faire, Ulf, Delgado, Graciela, Dominiczak, Anna F, Doney, Alex SF, Drenos, Fotios, Edkins, Sarah, Eicher, John D, Elosua, Roberto, Enroth, Stefan, Erdmann, Jeanette, Eriksson, Per, Esko, Tonu, Evangelou, Evangelos, Evans, Alun, Fall, Tove, Farrall, Martin, Felix, Janine F, Ferrières, Jean, Ferrucci, Luigi, Fornage, Myriam, Forrester, Terrence, Franceschini, Nora, Franco, Oscar H, and Franco-Cereceda, Anders

Subjects
Biological Sciences, Genetics, Biotechnology, 2.1 Biological and endogenous factors, Aetiology, Cardiovascular, Asian People, Black People, Blood Pressure, Cells, Cultured, Genome-Wide Association Study, Humans, Hypertension, Microarray Analysis, Polymorphism, Single Nucleotide, CHARGE-EchoGen consortium, CHARGE-HF consortium, Wellcome Trust Case Control Consortium, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

To dissect the genetic architecture of blood pressure and assess effects on target organ damage, we analyzed 128,272 SNPs from targeted and genome-wide arrays in 201,529 individuals of European ancestry, and genotypes from an additional 140,886 individuals were used for validation. We identified 66 blood pressure-associated loci, of which 17 were new; 15 harbored multiple distinct association signals. The 66 index SNPs were enriched for cis-regulatory elements, particularly in vascular endothelial cells, consistent with a primary role in blood pressure control through modulation of vascular tone across multiple tissues. The 66 index SNPs combined in a risk score showed comparable effects in 64,421 individuals of non-European descent. The 66-SNP blood pressure risk score was significantly associated with target organ damage in multiple tissues but with minor effects in the kidney. Our findings expand current knowledge of blood pressure-related pathways and highlight tissues beyond the classical renal system in blood pressure regulation.

Published
2016

29. 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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30. Pharmacogenetic meta-analysis of genome-wide association studies of LDL cholesterol response to statins.

Author

Postmus, Iris, Trompet, Stella, Deshmukh, Harshal A, Barnes, Michael R, Li, Xiaohui, Warren, Helen R, Chasman, Daniel I, Zhou, Kaixin, Arsenault, Benoit J, Donnelly, Louise A, Wiggins, Kerri L, Avery, Christy L, Griffin, Paula, Feng, QiPing, Taylor, Kent D, Li, Guo, Evans, Daniel S, Smith, Albert V, de Keyser, Catherine E, Johnson, Andrew D, de Craen, Anton JM, Stott, David J, Buckley, Brendan M, Ford, Ian, Westendorp, Rudi GJ, Slagboom, P Eline, Sattar, Naveed, Munroe, Patricia B, Sever, Peter, Poulter, Neil, Stanton, Alice, Shields, Denis C, O'Brien, Eoin, Shaw-Hawkins, Sue, Chen, Y-D Ida, Nickerson, Deborah A, Smith, Joshua D, Dubé, Marie Pierre, Boekholdt, S Matthijs, Hovingh, G Kees, Kastelein, John JP, McKeigue, Paul M, Betteridge, John, Neil, Andrew, Durrington, Paul N, Doney, Alex, Carr, Fiona, Morris, Andrew, McCarthy, Mark I, Groop, Leif, Ahlqvist, Emma, Welcome Trust Case Control Consortium, Bis, Joshua C, Rice, Kenneth, Smith, Nicholas L, Lumley, Thomas, Whitsel, Eric A, Stürmer, Til, Boerwinkle, Eric, Ngwa, Julius S, O'Donnell, Christopher J, Vasan, Ramachandran S, Wei, Wei-Qi, Wilke, Russell A, Liu, Ching-Ti, Sun, Fangui, Guo, Xiuqing, Heckbert, Susan R, Post, Wendy, Sotoodehnia, Nona, Arnold, Alice M, Stafford, Jeanette M, Ding, Jingzhong, Herrington, David M, Kritchevsky, Stephen B, Eiriksdottir, Gudny, Launer, Leonore J, Harris, Tamara B, Chu, Audrey Y, Giulianini, Franco, MacFadyen, Jean G, Barratt, Bryan J, Nyberg, Fredrik, Stricker, Bruno H, Uitterlinden, André G, Hofman, Albert, Rivadeneira, Fernando, Emilsson, Valur, Franco, Oscar H, Ridker, Paul M, Gudnason, Vilmundur, Liu, Yongmei, Denny, Joshua C, Ballantyne, Christie M, Rotter, Jerome I, Adrienne Cupples, L, Psaty, Bruce M, Palmer, Colin NA, Tardif, Jean-Claude, and Colhoun, Helen M

Subjects
Welcome Trust Case Control Consortium, Humans, Hydroxymethylglutaryl-CoA Reductase Inhibitors, Pharmacogenetics, Polymorphism, Single Nucleotide, Cholesterol, LDL, Genome-Wide Association Study, Cholesterol, LDL, Polymorphism, Single Nucleotide
Abstract

Statins effectively lower LDL cholesterol levels in large studies and the observed interindividual response variability may be partially explained by genetic variation. Here we perform a pharmacogenetic meta-analysis of genome-wide association studies (GWAS) in studies addressing the LDL cholesterol response to statins, including up to 18,596 statin-treated subjects. We validate the most promising signals in a further 22,318 statin recipients and identify two loci, SORT1/CELSR2/PSRC1 and SLCO1B1, not previously identified in GWAS. Moreover, we confirm the previously described associations with APOE and LPA. Our findings advance the understanding of the pharmacogenetic architecture of statin response.

Published
2014

31. Multi-omics subgroups associated with glycaemic deterioration in type 2 diabetes: an IMI-RHAPSODY Study.

Author

Shiying Li, Dragan, Iulian, Tran, Van Du T., Chun Ho Fung, Kuznetsov, Dmitry, Hansen, Michael K., Beulens, Joline W. J., t Hart, Leen M., Slieker, Roderick C., Donnelly, Louise A., Gerl, Mathias J., Klose, Christian, Mehl, Florence, Simons, Kai, Elders, Petra J. M., Pearson, Ewan R., Rutter, Guy A., and Ibberson, Mark

Subjects
TYPE 2 diabetes, MULTIOMICS, INTERLEUKIN receptors, BLOOD lipids, INSULIN, INSULIN sensitivity, INSULIN aspart
Abstract

Introduction: Type 2 diabetes (T2D) onset, progression and outcomes differ substantially between individuals. Multi-omics analyses may allow a deeper understanding of these differences and ultimately facilitate personalised treatments. Here, in an unsupervised "bottom-up" approach, we attempt to group T2D patients based solely on -omics data generated from plasma. Methods: Circulating plasma lipidomic and proteomic data from two independent clinical cohorts, Hoorn Diabetes Care System (DCS) and Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS), were analysed using Similarity Network Fusion. The resulting patient network was analysed with Logistic and Cox regression modelling to explore relationships between plasma -omic profiles and clinical characteristics. Results: From a total of 1,134 subjects in the two cohorts, levels of 180 circulating plasma lipids and 1195 proteins were used to separate patients into two subgroups. These differed in terms of glycaemic deterioration (Hazard Ratio=0.56;0.73), insulin sensitivity and secretion (C-peptide, p=3.7e-11;2.5e-06, DCS and GoDARTS, respectively; Homeostatic model assessment 2 (HOMA2)-B; -IR; -S, p=0.0008;4.2e-11;1.1e-09, only in DCS). The main molecular signatures separating the two groups included triacylglycerols, sphingomyelin, testican-1 and interleukin 18 receptor. Conclusions: Using an unsupervised network-based fusion method on plasma lipidomics and proteomics data from two independent cohorts, we were able to identify two subgroups of T2D patients differing in terms of disease severity. The molecular signatures identified within these subgroups provide insights into disease mechanisms and possibly new prognostic markers for T2D. [ABSTRACT FROM AUTHOR]

Published
2024
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36. Nodal analysis determined nasal mucosal-associated mediators were associated with accelerated clinical recovery from SARS-CoV-2 infection

Author

Cass, Steven, primary, Baker, Jonathan, additional, Mwasuku, Christine, additional, Ramakrishnan, Sanjay, additional, Mahdi, Mahdi, additional, Barnes, Peter, additional, Donnelly, Louise, additional, Russell, Richard, additional, Nicolau Jr., Dan, additional, Martinez-Nunez, Rocio, additional, and Bafadhel, Mona, additional

Published
2023
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39. Discovery and refinement of loci associated with lipid levels

Author

Willer, Cristen J, Schmidt, Ellen M, Sengupta, Sebanti, Peloso, Gina M, Gustafsson, Stefan, Kanoni, Stavroula, Ganna, Andrea, Chen, Jin, Buchkovich, Martin L, Mora, Samia, Beckmann, Jacques S, Bragg-Gresham, Jennifer L, Chang, Hsing-Yi, Demirkan, Ayse, Den Hertog, Heleen M, Do, Ron, Donnelly, Louise A, Ehret, Georg B, Esko, Tonu, Feitosa, Mary F, Ferreira, Teresa, Fischer, Krista, Fontanillas, Pierre, Fraser, Ross M, Freitag, Daniel F, Gurdasani, Deepti, Heikkila, Kauko, Hyppoenen, Elina, Isaacs, Aaron, Jackson, Anne U, Johansson, Asa, Johnson, Toby, Kaakinen, Marika, Kettunen, Johannes, Kleber, Marcus E, Li, Xiaohui, Luan, Jian'an, Lyytikainen, Leo-Pekka, Magnusson, Patrik KE, Mangino, Massimo, Mihailov, Evelin, Montasser, May E, Mueller-Nurasyid, Martina, Nolte, Ilja M, O'Connell, Jeffrey R, Palmer, Cameron D, Perola, Markus, Petersen, Ann-Kristin, Sanna, Serena, Saxena, Richa, Service, Susan K, Shah, Sonia, Shungin, Dmitry, Sidore, Carlo, Song, Ci, Strawbridge, Rona J, Surakka, Ida, Tanaka, Toshiko, Teslovich, Tanya M, Thorleifsson, Gudmar, Van den Herik, Evita G, Voight, Benjamin F, Volcik, Kelly A, Waite, Lindsay L, Wong, Andrew, Wu, Ying, Zhang, Weihua, Absher, Devin, Asiki, Gershim, Barroso, Ines, Been, Latonya F, Bolton, Jennifer L, Bonnycastle, Lori L, Brambilla, Paolo, Burnett, Mary S, Cesana, Giancarlo, Dimitriou, Maria, Doney, Alex SF, Doering, Angela, Elliott, Paul, Epstein, Stephen E, Eyjolfsson, Gudmundur Ingi, Gigante, Bruna, Goodarzi, Mark O, Grallert, Harald, Gravito, Martha L, Groves, Christopher J, Hallmans, Goran, Hartikainen, Anna-Liisa, Hayward, Caroline, Hernandez, Dena, Hicks, Andrew A, Holm, Hilma, Hung, Yi-Jen, Illig, Thomas, Jones, Michelle R, Kaleebu, Pontiano, Kastelein, John JP, Khaw, Kay-Tee, and Kim, Eric

Subjects
Cardiovascular, Human Genome, Genetics, 2.1 Biological and endogenous factors, Aetiology, Asian People, Black People, Cholesterol, HDL, Cholesterol, LDL, Coronary Artery Disease, Genetic Predisposition to Disease, Genome-Wide Association Study, Genotype, Humans, Lipids, Triglycerides, White People, Global Lipids Genetics Consortium, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

Levels of low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, triglycerides and total cholesterol are heritable, modifiable risk factors for coronary artery disease. To identify new loci and refine known loci influencing these lipids, we examined 188,577 individuals using genome-wide and custom genotyping arrays. We identify and annotate 157 loci associated with lipid levels at P < 5 × 10(-8), including 62 loci not previously associated with lipid levels in humans. Using dense genotyping in individuals of European, East Asian, South Asian and African ancestry, we narrow association signals in 12 loci. We find that loci associated with blood lipid levels are often associated with cardiovascular and metabolic traits, including coronary artery disease, type 2 diabetes, blood pressure, waist-hip ratio and body mass index. Our results demonstrate the value of using genetic data from individuals of diverse ancestry and provide insights into the biological mechanisms regulating blood lipids to guide future genetic, biological and therapeutic research.

Published
2013

40. Common variants associated with plasma triglycerides and risk for coronary artery disease

Author

Do, Ron, Willer, Cristen J, Schmidt, Ellen M, Sengupta, Sebanti, Gao, Chi, Peloso, Gina M, Gustafsson, Stefan, Kanoni, Stavroula, Ganna, Andrea, Chen, Jin, Buchkovich, Martin L, Mora, Samia, Beckmann, Jacques S, Bragg-Gresham, Jennifer L, Chang, Hsing-Yi, Demirkan, Ayşe, Den Hertog, Heleen M, Donnelly, Louise A, Ehret, Georg B, Esko, Tõnu, Feitosa, Mary F, Ferreira, Teresa, Fischer, Krista, Fontanillas, Pierre, Fraser, Ross M, Freitag, Daniel F, Gurdasani, Deepti, Heikkilä, Kauko, Hyppönen, Elina, Isaacs, Aaron, Jackson, Anne U, Johansson, Åsa, Johnson, Toby, Kaakinen, Marika, Kettunen, Johannes, Kleber, Marcus E, Li, Xiaohui, Luan, Jian'an, Lyytikäinen, Leo-Pekka, Magnusson, Patrik KE, Mangino, Massimo, Mihailov, Evelin, Montasser, May E, Müller-Nurasyid, Martina, Nolte, Ilja M, O'Connell, Jeffrey R, Palmer, Cameron D, Perola, Markus, Petersen, Ann-Kristin, Sanna, Serena, Saxena, Richa, Service, Susan K, Shah, Sonia, Shungin, Dmitry, Sidore, Carlo, Song, Ci, Strawbridge, Rona J, Surakka, Ida, Tanaka, Toshiko, Teslovich, Tanya M, Thorleifsson, Gudmar, Van den Herik, Evita G, Voight, Benjamin F, Volcik, Kelly A, Waite, Lindsay L, Wong, Andrew, Wu, Ying, Zhang, Weihua, Absher, Devin, Asiki, Gershim, Barroso, Inês, Been, Latonya F, Bolton, Jennifer L, Bonnycastle, Lori L, Brambilla, Paolo, Burnett, Mary S, Cesana, Giancarlo, Dimitriou, Maria, Doney, Alex SF, Döring, Angela, Elliott, Paul, Epstein, Stephen E, Eyjolfsson, Gudmundur Ingi, Gigante, Bruna, Goodarzi, Mark O, Grallert, Harald, Gravito, Martha L, Groves, Christopher J, Hallmans, Göran, Hartikainen, Anna-Liisa, Hayward, Caroline, Hernandez, Dena, Hicks, Andrew A, Holm, Hilma, Hung, Yi-Jen, Illig, Thomas, Jones, Michelle R, Kaleebu, Pontiano, Kastelein, John JP, and Khaw, Kay-Tee

Subjects
Heart Disease - Coronary Heart Disease, Atherosclerosis, Prevention, Heart Disease, Cardiovascular, 2.1 Biological and endogenous factors, Aetiology, Biological Transport, Cholesterol, HDL, Cholesterol, LDL, Coronary Artery Disease, Humans, Polymorphism, Single Nucleotide, Risk Factors, Triglycerides, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

Triglycerides are transported in plasma by specific triglyceride-rich lipoproteins; in epidemiological studies, increased triglyceride levels correlate with higher risk for coronary artery disease (CAD). However, it is unclear whether this association reflects causal processes. We used 185 common variants recently mapped for plasma lipids (P < 5 × 10(-8) for each) to examine the role of triglycerides in risk for CAD. First, we highlight loci associated with both low-density lipoprotein cholesterol (LDL-C) and triglyceride levels, and we show that the direction and magnitude of the associations with both traits are factors in determining CAD risk. Second, we consider loci with only a strong association with triglycerides and show that these loci are also associated with CAD. Finally, in a model accounting for effects on LDL-C and/or high-density lipoprotein cholesterol (HDL-C) levels, the strength of a polymorphism's effect on triglyceride levels is correlated with the magnitude of its effect on CAD risk. These results suggest that triglyceride-rich lipoproteins causally influence risk for CAD.

Published
2013

42. Virtual help seeking in infertility

Author

Donnelly, Louise Sian

Subjects
020, Psychology
Abstract

The aim of this thesis is to explore the infertile individual's use of the Internet and issues surrounding the debate on whether the Internet proves to be a useful tool or one that hinders healthcare. This was achieved by conducting a total of five studies that take a mixed methods and pragmatic approach to answering the overarching questions of the project. The first study explores the strengths and weaknesses of using the Internet for health. The aim of this is to investigate the reasons why people use the Internet, the frequency of use and importantly why it has become a tool in healthcare. The data was provided by four focus groups and was analysed using thematic analysis. The second study aims to extend this investigation to a wider population and to explore the attitudes of individuals toward ehealth by use of a questionnaire. The third study explores the function and impact of language on infertility and fertility website homepages by analysing the pages using discourse analysis. The fourth study then investigates the usability of these websites by analysing eye movements across the pages, responses to the system usability scale and by individual interviews. The final study in this thesis takes the issues of Internet use for infertile individuals further. It explores the experiential accounts of four women with an infertility diagnosis, who regularly use the Internet. It uses narrative analysis to explore the reasons for using the Internet, what they have gained from it and the impact that it has had on their lives. This thesis has identified that patient identity is changing and that this is aided by access to ehealth information. Participants were attracted to the Internet because of its immediacy, its convenience and because enables them to make informed decisions by accessing both medical information and sharing experiences with other infertile individuals.

Published
2009
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44. Phenotype-based targeted treatment of SGLT2 inhibitors and GLP-1 receptor agonists in type 2 diabetes

Author

Cardoso, Pedro, primary, Young, Katie G., additional, Nair, Anand T.N., additional, Hopkins, Rhian, additional, McGovern, Andrew P, additional, Haider, Eram, additional, Karunaratne, Piyumanga, additional, Donnelly, Louise, additional, Mateen, Bilal A., additional, Sattar, Naveed, additional, Holman, Rury R., additional, Bowden, Jack, additional, Hattersley, Andrew T., additional, Pearson, Ewan R., additional, Jones, Angus G., additional, Shields, Beverley M., additional, McKinley, Trevelyan J., additional, and Dennis, John M., additional

Published
2023
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