Search

Your search keyword '"Hingorani, Aroon"' showing total 1,753 results
Start Over Author "Hingorani, Aroon"
1,753 results on '"Hingorani, Aroon"'

1. Multi-ancestry genome-wide study identifies effector genes and druggable pathways for coronary artery calcification

Author

Kavousi, Maryam, Bos, Maxime M, Barnes, Hanna J, Lino Cardenas, Christian L, Wong, Doris, Lu, Haojie, Hodonsky, Chani J, Landsmeer, Lennart PL, Turner, Adam W, Kho, Minjung, Hasbani, Natalie R, de Vries, Paul S, Bowden, Donald W, Chopade, Sandesh, Deelen, Joris, Benavente, Ernest Diez, Guo, Xiuqing, Hofer, Edith, Hwang, Shih-Jen, Lutz, Sharon M, Lyytikäinen, Leo-Pekka, Slenders, Lotte, Smith, Albert V, Stanislawski, Maggie A, van Setten, Jessica, Wong, Quenna, Yanek, Lisa R, Becker, Diane M, Beekman, Marian, Budoff, Matthew J, Feitosa, Mary F, Finan, Chris, Hilliard, Austin T, Kardia, Sharon LR, Kovacic, Jason C, Kral, Brian G, Langefeld, Carl D, Launer, Lenore J, Malik, Shaista, Hoesein, Firdaus AA Mohamed, Mokry, Michal, Schmidt, Reinhold, Smith, Jennifer A, Taylor, Kent D, Terry, James G, van der Grond, Jeroen, van Meurs, Joyce, Vliegenthart, Rozemarijn, Xu, Jianzhao, Young, Kendra A, Zilhão, Nuno R, Zweiker, Robert, Assimes, Themistocles L, Becker, Lewis C, Bos, Daniel, Carr, J Jeffrey, Cupples, L Adrienne, de Kleijn, Dominique PV, de Winther, Menno, den Ruijter, Hester M, Fornage, Myriam, Freedman, Barry I, Gudnason, Vilmundur, Hingorani, Aroon D, Hokanson, John E, Ikram, M Arfan, Išgum, Ivana, Jacobs, David R, Kähönen, Mika, Lange, Leslie A, Lehtimäki, Terho, Pasterkamp, Gerard, Raitakari, Olli T, Schmidt, Helena, Slagboom, P Eline, Uitterlinden, André G, Vernooij, Meike W, Bis, Joshua C, Franceschini, Nora, Psaty, Bruce M, Post, Wendy S, Rotter, Jerome I, Björkegren, Johan LM, O’Donnell, Christopher J, Bielak, Lawrence F, Peyser, Patricia A, Malhotra, Rajeev, van der Laan, Sander W, and Miller, Clint L

Subjects
Biological Sciences, Genetics, Heart Disease - Coronary Heart Disease, Human Genome, Cardiovascular, Prevention, Heart Disease, Atherosclerosis, 2.1 Biological and endogenous factors, Aetiology, Humans, Black People, Coronary Artery Disease, Genome-Wide Association Study, Risk Factors, European People, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

Coronary artery calcification (CAC), a measure of subclinical atherosclerosis, predicts future symptomatic coronary artery disease (CAD). Identifying genetic risk factors for CAC may point to new therapeutic avenues for prevention. Currently, there are only four known risk loci for CAC identified from genome-wide association studies (GWAS) in the general population. Here we conducted the largest multi-ancestry GWAS meta-analysis of CAC to date, which comprised 26,909 individuals of European ancestry and 8,867 individuals of African ancestry. We identified 11 independent risk loci, of which eight were new for CAC and five had not been reported for CAD. These new CAC loci are related to bone mineralization, phosphate catabolism and hormone metabolic pathways. Several new loci harbor candidate causal genes supported by multiple lines of functional evidence and are regulators of smooth muscle cell-mediated calcification ex vivo and in vitro. Together, these findings help refine the genetic architecture of CAC and extend our understanding of the biological and potential druggable pathways underlying CAC.

Published
2023

5. Genome-wide association meta-analysis of spontaneous coronary artery dissection identifies risk variants and genes related to artery integrity and tissue-mediated coagulation.

Author

Adlam, David, Berrandou, Takiy-Eddine, Georges, Adrien, Nelson, Christopher, Giannoulatou, Eleni, Henry, Joséphine, Ma, Lijiang, Blencowe, Montgomery, Turley, Tamiel, Yang, Min-Lee, Chopade, Sandesh, Finan, Chris, Braund, Peter, Sadeg-Sayoud, Ines, Iismaa, Siiri, Kosel, Matthew, Zhou, Xiang, Hamby, Stephen, Cheng, Jenny, Liu, Lu, Tarr, Ingrid, Muller, David, dEscamard, Valentina, King, Annette, Brunham, Liam, Baranowska-Clarke, Ania, Debette, Stéphanie, Amouyel, Philippe, Olin, Jeffrey, Patil, Snehal, Hesselson, Stephanie, Junday, Keerat, Kanoni, Stavroula, Aragam, Krishna, Butterworth, Adam, Tweet, Marysia, Gulati, Rajiv, Combaret, Nicolas, Kadian-Dodov, Daniella, Kalman, Jonathan, Fatkin, Diane, Hingorani, Aroon, Saw, Jacqueline, Webb, Tom, Hayes, Sharonne, Yang, Xia, Ganesh, Santhi, Olson, Timothy, Kovacic, Jason, Graham, Robert, Samani, Nilesh, and Bouatia-Naji, Nabila

Subjects
Humans, Female, Genome-Wide Association Study, Vascular Diseases, Coronary Artery Disease, Myocardial Infarction
Abstract

Spontaneous coronary artery dissection (SCAD) is an understudied cause of myocardial infarction primarily affecting women. It is not known to what extent SCAD is genetically distinct from other cardiovascular diseases, including atherosclerotic coronary artery disease (CAD). Here we present a genome-wide association meta-analysis (1,917 cases and 9,292 controls) identifying 16 risk loci for SCAD. Integrative functional annotations prioritized genes that are likely to be regulated in vascular smooth muscle cells and artery fibroblasts and implicated in extracellular matrix biology. One locus containing the tissue factor gene F3, which is involved in blood coagulation cascade initiation, appears to be specific for SCAD risk. Several associated variants have diametrically opposite associations with CAD, suggesting that shared biological processes contribute to both diseases, but through different mechanisms. We also infer a causal role for high blood pressure in SCAD. Our findings provide novel pathophysiological insights involving arterial integrity and tissue-mediated coagulation in SCAD and set the stage for future specific therapeutics and preventions.

Published
2023

7. Biomedical consequences of elevated cholesterol-containing lipoproteins and apolipoproteins on cardiovascular and non-cardiovascular outcomes

Author

Schmidt, Amand F., Joshi, Roshni, Gordillo-Marañón, Maria, Drenos, Fotios, Charoen, Pimphen, Giambartolomei, Claudia, Bis, Joshua C., Gaunt, Tom R., Hughes, Alun D., Lawlor, Deborah A., Wong, Andrew, Price, Jackie F., Chaturvedi, Nishi, Wannamethee, Goya, Franceschini, Nora, Kivimaki, Mika, Hingorani, Aroon D., and Finan, Chris

Published
2023
Full Text
View/download PDF

10. Finding genetically-supported drug targets for Parkinson’s disease using Mendelian randomization of the druggable genome

Author

Storm, Catherine S, Kia, Demis A, Almramhi, Mona M, Bandres-Ciga, Sara, Finan, Chris, Hingorani, Aroon D, and Wood, Nicholas W

Subjects
Biotechnology, Genetics, Prevention, Aging, Neurosciences, Brain Disorders, Parkinson's Disease, Neurodegenerative, 2.1 Biological and endogenous factors, Development of treatments and therapeutic interventions, Aetiology, 5.1 Pharmaceuticals, Neurological, Good Health and Well Being, Brain, Case-Control Studies, Cohort Studies, Disease Progression, Gene Expression Regulation, Genetic Predisposition to Disease, Genetic Variation, Genome, Human, Humans, Mendelian Randomization Analysis, Parkinson Disease, Quantitative Trait Loci, Risk Factors, International Parkinson’s Disease Genomics Consortium
Abstract

Parkinson's disease is a neurodegenerative movement disorder that currently has no disease-modifying treatment, partly owing to inefficiencies in drug target identification and validation. We use Mendelian randomization to investigate over 3,000 genes that encode druggable proteins and predict their efficacy as drug targets for Parkinson's disease. We use expression and protein quantitative trait loci to mimic exposure to medications, and we examine the causal effect on Parkinson's disease risk (in two large cohorts), age at onset and progression. We propose 23 drug-targeting mechanisms for Parkinson's disease, including four possible drug repurposing opportunities and two drugs which may increase Parkinson's disease risk. Of these, we put forward six drug targets with the strongest Mendelian randomization evidence. There is remarkably little overlap between our drug targets to reduce Parkinson's disease risk versus progression, suggesting different molecular mechanisms. Drugs with genetic support are considerably more likely to succeed in clinical trials, and we provide compelling genetic evidence and an analysis pipeline to prioritise Parkinson's disease drug development.

Published
2021

11. Identifying and visualising multimorbidity and comorbidity patterns in patients in the English National Health Service: a population-based study

Author

Alexander, Daniel C, Asiimwe, Innocent G, Ball, Simon, Bennett, Frances, Borges, Maria Carolina, Butterworth, Adam, Chaturvedi, Nishi, Chopade, Sandesh, Clarkson, Christopher, Cox, Martin, Dale, Caroline, Denaxas, Spiros, Dunca, Diana, Engmann, Jorgen E, Fernandez-Sanles, Alba, Finan, Chris, Fitzpatrick, Natalie, Gallagher, Jean, Gonzalez-Izquierdo, Arturo, Gratton, Jasmine, Gross, Christian, Hemingway, Harry, Henry, Albert, Hidajat, Mira, Hingorani, Aroon, Hukerikar, Nikita, Jorgensen, Andrea, Joshi, Roshni, Katsoulis, Michail, Kuan, Valerie, Kumar, Rashmi, Lai, Alvina G, Langenberg, Claudia, Lawlor, Deborah, Mancini, Mary, Miller, Diane, Ogden, Margaret, Ozyigit, Eda B, Patel, Shilpa, Pirmohamed, Munir, Roberts, Amanda, Ryan, David, Schmidt, Amand F, Shah, Anoop D, Shah, Tina, Sofat, Reecha, Takhar, Rohan, Torralbo, Ana, Ullah, Ayath, Walker, Lauren E, Warwick, Alasdair, Wheeler, Eleanor, Wright, Victoria L, Wu, Honghan, Zwierzyna, Magdalena, Patalay, Praveetha, Nitsch, Dorothea, Mathur, Rohini, Partridge, Linda, Wong, Ian C K, Hingorani, Melanie, and Hingorani, Aroon D

Published
2023
Full Text
View/download PDF

12. Proteogenomic Data Integration Reveals CXCL10 as a Potentially Downstream Causal Mediator for IL-6 Signaling on Atherosclerosis

Author

Prapiadou, Savvina, Živković, Luka, Thorand, Barbara, George, Marc J., van der Laan, Sander W., Malik, Rainer, Herder, Christian, Koenig, Wolfgang, Ueland, Thor, Kleveland, Ola, Aukrust, Pål, Gullestad, Lars, Bernhagen, Jürgen, Pasterkamp, Gerard, Peters, Annette, Hingorani, Aroon D., Rosand, Jonathan, Dichgans, Martin, Anderson, Christopher D., and Georgakis, Marios K.

Published
2023
Full Text
View/download PDF

15. A national initiative in data science for health: an evaluation of the UK Farr Institute

Author

Hemingway, H, Lyons, R, Li, Q, Buchan, I, Ainsworth, J, Pell, J, Morris, A, Barnes, Michael R, Bedford, Helen, Bennie, Marion, Blandford, Ann, Briggs, Andy, Brocklehurst, Peter, Brophy, Sinead, Brown, Gavin, Burton, Paul, Butler, Christopher, Capewell, Simon, Carpenter, James, Carroll, John, Cassell, Jackie A, Castillo, Fortunato, Catchpole, Mike, Caulfield, Mark, Colhoun, Helen, Coveney, Peter, Cunningham-Burley, Sarah, Custovic, Adnan, Deanfield, John, Denaxas, Spiros, Dennis, Michael, Dezateux, Carol, Dibben, Chris, Diggle, Peter, Dixon, Will, Dunn, Graham, Emam, Khaled El, Fone, David, Ford, David, Ford, Ian, Frank, John, Freemantle, Nick, Gabbe, Belinda, Gallacher, John, Gibson, Martin, Gilbert, Ruth, Gissler, Mika, Goble, Carol, Goldberg, Andy, Gravenor, Mike, Gunnell, David, Hannaford, Phil, Hayward, Andrew, Hickman, Matthew, Hingorani, Aroon, Holm, Soren, Holman, Cashel, John, Gareth, John, Ann, Jones, Kerina, Kalra, Dipak, Laurie, Graeme, Lewis, Shon, Lloyd, Keith, Lowe, Sarah, McCowan, Colin, Macleod, John, Martin, Richard M, Moore, Anthony, Moore, Laurence, Nazareth, Irwin, Nenadic, Goran, Paranjothy, Shantini, Parmar, Max, Pebody, Richard, Petersen, Steffen, Petersen, Irene, Pillay, Deenan, Preen, David, Pickett, Kate, Pritchard-Jones, Kathy, Przulj, Natasa, Renehan, Andrew, Roberts, Stephen, Robson, John, Rodgers, Sarah, Rossor, Martin, Russell, Ian, Shawe-Taylor, John, Sheikh, Aziz, Siebert, Stefan, Snooks, Helen, Sperrin, Matthew, Stephenson, Judith, Sullivan, Frank, Taylor, Chris, Taylor, Paul, Timmis, Adam, and Ward, Hester JT

Subjects
Information and Computing Sciences, Library and Information Studies, Health Sciences, Generic health relevance, Health sciences, Human society, Information and computing sciences
Abstract

ObjectiveTo evaluate the extent to which the inter-institutional, inter-disciplinary mobilisation of data and skills in the Farr Institute contributed to establishing the emerging field of data science for health in the UK.Design and outcome measuresWe evaluated evidence of six domains characterising a new field of science:defining central scientific challenges,demonstrating how the central challenges might be solved,creating novel interactions among groups of scientists,training new types of experts,re-organising universities,demonstrating impacts in society.We carried out citation, network and time trend analyses of publications, and a narrative review of infrastructure, methods and tools.SettingFour UK centres in London, North England, Scotland and Wales (23 university partners), 2013-2018.Results1. The Farr Institute helped define a central scientific challenge publishing a research corpus, demonstrating insights from electronic health record (EHR) and administrative data at each stage of the translational cycle in 593 papers with at least one Farr Institute author affiliation on PubMed. 2. The Farr Institute offered some demonstrations of how these scientific challenges might be solved: it established the first four ISO27001 certified trusted research environments in the UK, and approved more than 1000 research users, published on 102 unique EHR and administrative data sources, although there was no clear evidence of an increase in novel, sustained record linkages. The Farr Institute established open platforms for the EHR phenotyping algorithms and validations (>70 diseases, CALIBER). Sample sizes showed some evidence of increase but remained less than 10% of the UK population in primary care-hospital care linked studies. 3.The Farr Institute created novel interactions among researchers: the co-author publication network expanded from 944 unique co-authors (based on 67 publications in the first 30 months) to 3839 unique co-authors (545 papers in the final 30 months). 4. Training expanded substantially with 3 new masters courses, training >400 people at masters, short-course and leadership level and 48 PhD students. 5. Universities reorganised with 4/5 Centres established 27 new faculty (tenured) positions, 3 new university institutes. 6. Emerging evidence of impacts included: > 3200 citations for the 10 most cited papers and Farr research informed eight practice-changing clinical guidelines and policies relevant to the health of millions of UK citizens.ConclusionThe Farr Institute played a major role in establishing and growing the field of data science for health in the UK, with some initial evidence of benefits for health and healthcare. The Farr Institute has now expanded into Health Data Research (HDR) UK but key challenges remain including, how to network such activities internationally.

Published
2020

16. Plasma protein patterns as comprehensive indicators of health

Author

Williams, Stephen A, Kivimaki, Mika, Langenberg, Claudia, Hingorani, Aroon D, Casas, JP, Bouchard, Claude, Jonasson, Christian, Sarzynski, Mark A, Shipley, Martin J, Alexander, Leigh, Ash, Jessica, Bauer, Tim, Chadwick, Jessica, Datta, Gargi, DeLisle, Robert Kirk, Hagar, Yolanda, Hinterberg, Michael, Ostroff, Rachel, Weiss, Sophie, Ganz, Peter, and Wareham, Nicholas J

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Nutrition, Behavioral and Social Science, Obesity, Prevention, Heart Disease, Cardiovascular, Generic health relevance, Metabolic and endocrine, Good Health and Well Being, Adipose Tissue, Blood Proteins, Body Composition, Exercise, Female, Humans, Intra-Abdominal Fat, Life Style, Liver, Male, Precision Medicine, Risk Factors, Medical and Health Sciences, Immunology, Biomedical and clinical sciences, Health sciences
Abstract

Proteins are effector molecules that mediate the functions of genes1,2 and modulate comorbidities3-10, behaviors and drug treatments11. They represent an enormous potential resource for personalized, systemic and data-driven diagnosis, prevention, monitoring and treatment. However, the concept of using plasma proteins for individualized health assessment across many health conditions simultaneously has not been tested. Here, we show that plasma protein expression patterns strongly encode for multiple different health states, future disease risks and lifestyle behaviors. We developed and validated protein-phenotype models for 11 different health indicators: liver fat, kidney filtration, percentage body fat, visceral fat mass, lean body mass, cardiopulmonary fitness, physical activity, alcohol consumption, cigarette smoking, diabetes risk and primary cardiovascular event risk. The analyses were prospectively planned, documented and executed at scale on archived samples and clinical data, with a total of ~85 million protein measurements in 16,894 participants. Our proof-of-concept study demonstrates that protein expression patterns reliably encode for many different health issues, and that large-scale protein scanning12-16 coupled with machine learning is viable for the development and future simultaneous delivery of multiple measures of health. We anticipate that, with further validation and the addition of more protein-phenotype models, this approach could enable a single-source, individualized so-called liquid health check.

Published
2019

19. Quantitative, multiplexed, targeted proteomics for ascertaining variant specific SARS-CoV-2 antibody response

Author

Abbass, Hakam, Abiodun, Aderonke, Alfarih, Mashael, Alldis, Zoe, Altmann, Daniel M., Amin, Oliver E., Andiapen, Mervyn, Artico, Jessica, Augusto, João B., Baca, Georgina L., Bailey, Sasha N.L., Bhuva, Anish N., Boulter, Alex, Bowles, Ruth, Boyton, Rosemary J., Bracken, Olivia V., O’Brien, Ben, Brooks, Tim, Bullock, Natalie, Butler, David K., Captur, Gabriella, Carr, Olivia, Champion, Nicola, Chan, Carmen, Chandran, Aneesh, Coleman, Tom, Couto de Sousa, Jorge, Couto-Parada, Xose, Cross, Eleanor, Cutino-Moguel, Teresa, D’Arcangelo, Silvia, Davies, Rhodri H., Douglas, Brooke, Di Genova, Cecilia, Dieobi-Anene, Keenan, Diniz, Mariana O., Ellis, Anaya, Feehan, Karen, Finlay, Malcolm, Fontana, Marianna, Forooghi, Nasim, Francis, Sasha, Gibbons, Joseph M., Gillespie, David, Gilroy, Derek, Hamblin, Matt, Harker, Gabrielle, Hemingway, Georgia, Hewson, Jacqueline, Heywood, Wendy, Hickling, Lauren M., Hicks, Bethany, Hingorani, Aroon D., Howes, Lee, Itua, Ivie, Jardim, Victor, Lee, Wing-Yiu Jason, Jensen, Melaniepetra, Jones, Jessica, Jones, Meleri, Joy, George, Kapil, Vikas, Kelly, Caoimhe, Kurdi, Hibba, Lambourne, Jonathan, Lin, Kai-Min, Liu, Siyi, Lloyd, Aaron, Louth, Sarah, Maini, Mala K., Mandadapu, Vineela, Manisty, Charlotte, McKnight, Áine, Menacho, Katia, Mfuko, Celina, Mills, Kevin, Millward, Sebastian, Mitchelmore, Oliver, Moon, Christopher, Moon, James, Sandoval, Diana Muñoz, Murray, Sam M., Noursadeghi, Mahdad, Otter, Ashley, Pade, Corinna, Palma, Susana, Parker, Ruth, Patel, Kush, Pawarova, Mihaela, Petersen, Steffen E., Piniera, Brian, Pieper, Franziska P., Rannigan, Lisa, Rapala, Alicja, Reynolds, Catherine J., Richards, Amy, Robathan, Matthew, Rosenheim, Joshua, Rowe, Cathy, Royds, Matthew, West, Jane Sackville, Sambile, Genine, Schmidt, Nathalie M., Selman, Hannah, Semper, Amanda, Seraphim, Andreas, Simion, Mihaela, Smit, Angelique, Sugimoto, Michelle, Swadling, Leo, Taylor, Stephen, Temperton, Nigel, Thomas, Stephen, Thornton, George D., Treibel, Thomas A., Tucker, Art, Varghese, Ann, Veerapen, Jessry, Vijayakumar, Mohit, Warner, Tim, Welch, Sophie, White, Hannah, Wodehouse, Theresa, Wynne, Lucinda, Zahedi, Dan, Doykov, Ivan, Baldwin, Tomas, Spiewak, Justyna, Gilmour, Kimberly C., Áine McKnight, Treibel, Thomas, Moon, James C., Kevin Mills, and Heywood, Wendy E.

Published
2022
Full Text
View/download PDF

22. Evaluation of Machine Learning Methods to Predict Coronary Artery Disease Using Metabolomic Data

Author

Forssen, Henrietta, Patel, Riyaz S., Fitzpatrick, Natalie, Hingorani, Aroon, Timmis, Adam, Hemingway, Harry, and Denaxas, Spiros C.

Subjects
Computer Science - Learning
Abstract

Metabolomic data can potentially enable accurate, non-invasive and low-cost prediction of coronary artery disease. Regression-based analytical approaches however might fail to fully account for interactions between metabolites, rely on a priori selected input features and thus might suffer from poorer accuracy. Supervised machine learning methods can potentially be used in order to fully exploit the dimensionality and richness of the data. In this paper, we systematically implement and evaluate a set of supervised learning methods (L1 regression, random forest classifier) and compare them to traditional regression-based approaches for disease prediction using metabolomic data., Comment: Medical Informatics Europe (MIE2017)

Published
2017

26. Blood transcriptional biomarkers of acute viral infection for detection of pre-symptomatic SARS-CoV-2 infection: a nested, case-control diagnostic accuracy study

Author

Abbass, Hakam, Abiodun, Aderonke, Alfarih, Mashael, Alldis, Zoe, Altmann, Daniel M, Amin, Oliver E, Andiapen, Mervyn, Artico, Jessica, Augusto, João B, Baca, Georgiana L, Bailey, Sasha NL, Bhuva, Anish N, Boulter, Alex, Bowles, Ruth, Boyton, Rosemary J, Bracken, Olivia V, O'Brien, Ben, Brooks, Tim, Bullock, Natalie, Butler, David K, Captur, Gabriella, Champion, Nicola, Chan, Carmen, Chandran, Aneesh, Collier, David, Couto de Sousa, Jorge, Couto-Parada, Xose, Cutino-Moguel, Teresa, Davies, Rhodri H, Douglas, Brooke, Di Genova, Cecilia, Dieobi-Anene, Keenan, Diniz, Mariana O, Ellis, Anaya, Feehan, Karen, Finlay, Malcolm, Fontana, Marianna, Forooghi, Nasim, Gaier, Celia, Gibbons, Joseph M, Gilroy, Derek, Hamblin, Matt, Harker, Gabrielle, Hewson, Jacqueline, Hickling, Lauren M, Hingorani, Aroon D, Howes, Lee, Hughes, Alun, Hughes, Gemma, Hughes, Rebecca, Itua, Ivie, Jardim, Victor, Lee, Wing-Yiu Jason, Jensen, Melaniepetra, Jones, Jessica, Jones, Meleri, Joy, George, Kapil, Vikas, Kurdi, Hibba, Lambourne, Jonathan, Lin, Kai-Min, Louth, Sarah, Maini, Mala K, Mandadapu, Vineela, Manisty, Charlotte, McKnight, Áine, Menacho, Katia, Mfuko, Celina, Mitchelmore, Oliver, Moon, Christopher, Moon, James C, Munoz Sandoval, Diana, Murray, Sam M, Noursadeghi, Mahdad, Otter, Ashley, Pade, Corinna, Palma, Susana, Parker, Ruth, Patel, Kush, Pawarova, Babita, Petersen, Steffen E, Piniera, Brian, Pieper, Franziska P, Pope, Daniel, Prossora, Maria, Rannigan, Lisa, Rapala, Alicja, Reynolds, Catherine J, Richards, Amy, Robathan, Matthew, Rosenheim, Joshua, Sambile, Genine, Schmidt, Nathalie M, Semper, Amanda, Seraphim, Andreas, Simion, Mihaela, Smit, Angelique, Sugimoto, Michelle, Swadling, Leo, Taylor, Stephen, Temperton, Nigel, Thomas, Stephen, Thornton, George D, Treibel, Thomas A, Tucker, Art, Veerapen, Jessry, Vijayakumar, Mohit, Welch, Sophie, Wodehouse, Theresa, Wynne, Lucinda, Zahedi, Dan, Gupta, Rishi K, Bell, Lucy C, Guerra-Assuncao, Jose A, Pollara, Gabriele, Whelan, Matthew, McKnight, Aine, and Chain, Benjamin M

Published
2021
Full Text
View/download PDF

28. Proteogenomic Data Integration Reveals CXCL10 as a Potentially Downstream Causal Mediator for IL-6 Signaling on Atherosclerosis

Author

CDL Onderzoek Pasterkamp, Circulatory Health, Centraal Diagnostisch Laboratorium, Prapiadou, Savvina, Živković, Luka, Thorand, Barbara, George, Marc J, van der Laan, Sander W, Malik, Rainer, Herder, Christian, Koenig, Wolfgang, Ueland, Thor, Kleveland, Ola, Aukrust, Pål, Gullestad, Lars, Bernhagen, Jürgen, Pasterkamp, Gerard, Peters, Annette, Hingorani, Aroon D, Rosand, Jonathan, Dichgans, Martin, Anderson, Christopher D, Georgakis, Marios K, CDL Onderzoek Pasterkamp, Circulatory Health, Centraal Diagnostisch Laboratorium, Prapiadou, Savvina, Živković, Luka, Thorand, Barbara, George, Marc J, van der Laan, Sander W, Malik, Rainer, Herder, Christian, Koenig, Wolfgang, Ueland, Thor, Kleveland, Ola, Aukrust, Pål, Gullestad, Lars, Bernhagen, Jürgen, Pasterkamp, Gerard, Peters, Annette, Hingorani, Aroon D, Rosand, Jonathan, Dichgans, Martin, Anderson, Christopher D, and Georgakis, Marios K

Published
2024

29. Therapeutic potential of IL6R blockade for the treatment of sepsis and sepsis-related death: A mendelian randomisation study

Author

Hamilton, Fergus W., Thomas, Matt, Arnold, David, Palmer, Tom, Moran, Ed, Mentzer, Alexander J., Maskell, Nick, Baillie, Kenneth, Summers, Charlotte, Hingorani, Aroon, MacGowan, Alasdair, Khandaker, Golam M., Mitchell, Ruth, Davey Smith, George, Ghazal, Peter, and Timpson, Nicholas J.

Subjects
Interleukin-6 -- Health aspects -- Dosage and administration -- Testing, Sepsis -- Care and treatment -- Patient outcomes, Biological sciences
Abstract

Background Sepsis is characterised by dysregulated, life-threatening immune responses, which are thought to be driven by cytokines such as interleukin 6 (IL-6). Genetic variants in IL6R known to down-regulate IL-6 signalling are associated with improved Coronavirus Disease 2019 (COVID-19) outcomes, a finding later confirmed in randomised trials of IL-6 receptor antagonists (IL6RAs). We hypothesised that blockade of IL6R could also improve outcomes in sepsis. Methods and findings We performed a mendelian randomisation (MR) analysis using single nucleotide polymorphisms (SNPs) in and near IL6R to evaluate the likely causal effects of IL6R blockade on sepsis (primary outcome), sepsis severity, other infections, and COVID-19 (secondary outcomes). We weighted SNPs by their effect on CRP and combined results across them in inverse variance weighted meta-analysis, proxying the effect of IL6RA. Our outcomes were measured in UK Biobank, FinnGen, the COVID-19 Host Genetics Initiative (HGI), and the GenOSept and GainS consortium. We performed several sensitivity analyses to test assumptions of our methods, including utilising variants around CRP and gp130 in a similar analysis. In the UK Biobank cohort (N = 486,484, including 11,643 with sepsis), IL6R blockade was associated with a decreased risk of our primary outcome, sepsis (odds ratio (OR) = 0.80; 95% confidence interval (CI) 0.66 to 0.96, per unit of natural log-transformed CRP decrease). The size of this effect increased with severity, with larger effects on 28-day sepsis mortality (OR = 0.74; 95% CI 0.47 to 1.15); critical care admission with sepsis (OR = 0.48, 95% CI 0.30 to 0.78) and critical care death with sepsis (OR = 0.37, 95% CI 0.14 to 0.98). Similar associations were seen with severe respiratory infection: OR for pneumonia in critical care 0.69 (95% CI 0.49 to 0.97) and for sepsis survival in critical care (OR = 0.22; 95% CI 0.04 to 1.31) in the GainS and GenOSept consortium, although this result had a large degree of imprecision. We also confirm the previously reported protective effect of IL6R blockade on severe COVID-19 (OR = 0.69, 95% CI 0.57 to 0.84) in the COVID-19 HGI, which was of similar magnitude to that seen in sepsis. Sensitivity analyses did not alter our primary results. These results are subject to the limitations and assumptions of MR, which in this case reflects interpretation of these SNP effects as causally acting through blockade of IL6R, and reflect lifetime exposure to IL6R blockade, rather than the effect of therapeutic IL6R blockade. Conclusions IL6R blockade is causally associated with reduced incidence of sepsis. Similar but imprecisely estimated results supported a causal effect also on sepsis related mortality and critical care admission with sepsis. These effects are comparable in size to the effect seen in severe COVID-19, where IL-6 receptor antagonists were shown to improve survival. These data suggest that a randomised trial of IL-6 receptor antagonists in sepsis should be considered., Author(s): Fergus W. Hamilton 1,2,*, Matt Thomas 3, David Arnold 4, Tom Palmer 1, Ed Moran 2, Alexander J. Mentzer 5, Nick Maskell 4, Kenneth Baillie 6, Charlotte Summers 7, [...]

Published
2023
Full Text
View/download PDF

30. Uncovering genetic mechanisms of hypertension through multi-omic analysis of the kidney

Author

Eales, James M., Jiang, Xiao, Xu, Xiaoguang, Saluja, Sushant, Akbarov, Artur, Cano-Gamez, Eddie, McNulty, Michelle T., Finan, Christopher, Guo, Hui, Wystrychowski, Wojciech, Szulinska, Monika, Thomas, Huw B., Pramanik, Sanjeev, Chopade, Sandesh, Prestes, Priscilla R., Wise, Ingrid, Evangelou, Evangelos, Salehi, Mahan, Shakanti, Yusif, Ekholm, Mikael, Denniff, Matthew, Nazgiewicz, Alicja, Eichinger, Felix, Godfrey, Bradley, Antczak, Andrzej, Glyda, Maciej, Król, Robert, Eyre, Stephen, Brown, Jason, Berzuini, Carlo, Bowes, John, Caulfield, Mark, Zukowska-Szczechowska, Ewa, Zywiec, Joanna, Bogdanski, Pawel, Kretzler, Matthias, Woolf, Adrian S., Talavera, David, Keavney, Bernard, Maffia, Pasquale, Guzik, Tomasz J., O’Keefe, Raymond T., Trynka, Gosia, Samani, Nilesh J., Hingorani, Aroon, Sampson, Matthew G., Morris, Andrew P., Charchar, Fadi J., and Tomaszewski, Maciej

Published
2021
Full Text
View/download PDF

32. NMR metabolomic modeling of age and lifespan: A multicohort analysis.

Author

Lau, Chung‐Ho E., Manou, Maria, Markozannes, Georgios, Ala‐Korpela, Mika, Ben‐Shlomo, Yoav, Chaturvedi, Nish, Engmann, Jorgen, Gentry‐Maharaj, Aleksandra, Herzig, Karl‐Heinz, Hingorani, Aroon, Järvelin, Marjo‐Riitta, Kähönen, Mika, Kivimäki, Mika, Lehtimäki, Terho, Marttila, Saara, Menon, Usha, Munroe, Patricia B., Palaniswamy, Saranya, Providencia, Rui, and Raitakari, Olli

Subjects
METABOLOMICS, CHRONIC obstructive pulmonary disease, CAROTID intima-media thickness, NUCLEAR magnetic resonance spectroscopy, AGE
Abstract

Metabolomic age models have been proposed for the study of biological aging, however, they have not been widely validated. We aimed to assess the performance of newly developed and existing nuclear magnetic resonance spectroscopy (NMR) metabolomic age models for prediction of chronological age (CA), mortality, and age‐related disease. Ninety‐eight metabolic variables were measured in blood from nine UK and Finnish cohort studies (N ≈31,000 individuals, age range 24–86 years). We used nonlinear and penalized regression to model CA and time to all‐cause mortality. We examined associations of four new and two previously published metabolomic age models, with aging risk factors and phenotypes. Within the UK Biobank (N ≈102,000), we tested prediction of CA, incident disease (cardiovascular disease (CVD), type‐2 diabetes mellitus, cancer, dementia, and chronic obstructive pulmonary disease), and all‐cause mortality. Seven‐fold cross‐validated Pearson's r between metabolomic age models and CA ranged between 0.47 and 0.65 in the training cohort set (mean absolute error: 8–9 years). Metabolomic age models, adjusted for CA, were associated with C‐reactive protein, and inversely associated with glomerular filtration rate. Positively associated risk factors included obesity, diabetes, smoking, and physical inactivity. In UK Biobank, correlations of metabolomic age with CA were modest (r = 0.29–0.33), yet all metabolomic model scores predicted mortality (hazard ratios of 1.01 to 1.06/metabolomic age year) and CVD, after adjustment for CA. While metabolomic age models were only moderately associated with CA in an independent population, they provided additional prediction of morbidity and mortality over CA itself, suggesting their wider applicability. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

33. Proteogenomic Data Integration Reveals CXCL10 as a Potentially Downstream Causal Mediator for IL-6 Signaling on Atherosclerosis

Author

Prapiadou, Savvina, primary, Živković, Luka, additional, Thorand, Barbara, additional, George, Marc J., additional, van der Laan, Sander W., additional, Malik, Rainer, additional, Herder, Christian, additional, Koenig, Wolfgang, additional, Ueland, Thor, additional, Kleveland, Ola, additional, Aukrust, Pål, additional, Gullestad, Lars, additional, Bernhagen, Jürgen, additional, Pasterkamp, Gerard, additional, Peters, Annette, additional, Hingorani, Aroon D., additional, Rosand, Jonathan, additional, Dichgans, Martin, additional, Anderson, Christopher D., additional, and Georgakis, Marios K., additional

Published
2024
Full Text
View/download PDF

34. Sex-dimorphic genetic effects and novel loci for fasting glucose and insulin variability

Author

Lagou, Vasiliki, Mägi, Reedik, Hottenga, Jouke- Jan, Grallert, Harald, Perry, John R. B., Bouatia-Naji, Nabila, Marullo, Letizia, Rybin, Denis, Jansen, Rick, Min, Josine L., Dimas, Antigone S., Ulrich, Anna, Zudina, Liudmila, Gådin, Jesper R., Jiang, Longda, Faggian, Alessia, Bonnefond, Amélie, Fadista, Joao, Stathopoulou, Maria G., Isaacs, Aaron, Willems, Sara M., Navarro, Pau, Tanaka, Toshiko, Jackson, Anne U., Montasser, May E., O’Connell, Jeff R., Bielak, Lawrence F., Webster, Rebecca J., Saxena, Richa, Stafford, Jeanette M., Pourcain, Beate St, Timpson, Nicholas J., Salo, Perttu, Shin, So-Youn, Amin, Najaf, Smith, Albert V., Li, Guo, Verweij, Niek, Goel, Anuj, Ford, Ian, Johnson, Paul C. D., Johnson, Toby, Kapur, Karen, Thorleifsson, Gudmar, Strawbridge, Rona J., Rasmussen-Torvik, Laura J., Esko, Tõnu, Mihailov, Evelin, Fall, Tove, Fraser, Ross M., Mahajan, Anubha, Kanoni, Stavroula, Giedraitis, Vilmantas, Kleber, Marcus E., Silbernagel, Günther, Meyer, Julia, Müller-Nurasyid, Martina, Ganna, Andrea, Sarin, Antti-Pekka, Yengo, Loic, Shungin, Dmitry, Luan, Jian’an, Horikoshi, Momoko, An, Ping, Sanna, Serena, Boettcher, Yvonne, Rayner, N. William, Nolte, Ilja M., Zemunik, Tatijana, Iperen, Erik van, Kovacs, Peter, Hastie, Nicholas D., Wild, Sarah H., McLachlan, Stela, Campbell, Susan, Polasek, Ozren, Carlson, Olga, Egan, Josephine, Kiess, Wieland, Willemsen, Gonneke, Kuusisto, Johanna, Laakso, Markku, Dimitriou, Maria, Hicks, Andrew A., Rauramaa, Rainer, Bandinelli, Stefania, Thorand, Barbara, Liu, Yongmei, Miljkovic, Iva, Lind, Lars, Doney, Alex, Perola, Markus, Hingorani, Aroon, Kivimaki, Mika, Kumari, Meena, Bennett, Amanda J., Groves, Christopher J., Herder, Christian, Koistinen, Heikki A., Kinnunen, Leena, Faire, Ulf de, Bakker, Stephan J. L., Uusitupa, Matti, Palmer, Colin N. A., Jukema, J. Wouter, Sattar, Naveed, Pouta, Anneli, Snieder, Harold, Boerwinkle, Eric, Pankow, James S., Magnusson, Patrik K., Krus, Ulrika, Scapoli, Chiara, de Geus, Eco J. C. N., Blüher, Matthias, Wolffenbuttel, Bruce H. R., Province, Michael A., Abecasis, Goncalo R., Meigs, James B., Hovingh, G. Kees, Lindström, Jaana, Wilson, James F., Wright, Alan F., Dedoussis, George V., Bornstein, Stefan R., Schwarz, Peter E. H., Tönjes, Anke, Winkelmann, Bernhard R., Boehm, Bernhard O., März, Winfried, Metspalu, Andres, Price, Jackie F., Deloukas, Panos, Körner, Antje, Lakka, Timo A., Keinanen-Kiukaanniemi, Sirkka M., Saaristo, Timo E., Bergman, Richard N., Tuomilehto, Jaakko, Wareham, Nicholas J., Langenberg, Claudia, Männistö, Satu, Franks, Paul W., Hayward, Caroline, Vitart, Veronique, Kaprio, Jaakko, Visvikis-Siest, Sophie, Balkau, Beverley, Altshuler, David, Rudan, Igor, Stumvoll, Michael, Campbell, Harry, van Duijn, Cornelia M., Gieger, Christian, Illig, Thomas, Ferrucci, Luigi, Pedersen, Nancy L., Pramstaller, Peter P., Boehnke, Michael, Frayling, Timothy M., Shuldiner, Alan R., Peyser, Patricia A., Kardia, Sharon L. R., Palmer, Lyle J., Penninx, Brenda W., Meneton, Pierre, Harris, Tamara B., Navis, Gerjan, Harst, Pim van der, Smith, George Davey, Forouhi, Nita G., Loos, Ruth J. F., Salomaa, Veikko, Soranzo, Nicole, Boomsma, Dorret I., Groop, Leif, Tuomi, Tiinamaija, Hofman, Albert, Munroe, Patricia B., Gudnason, Vilmundur, Siscovick, David S., Watkins, Hugh, Lecoeur, Cecile, Vollenweider, Peter, Franco-Cereceda, Anders, Eriksson, Per, Jarvelin, Marjo-Riitta, Stefansson, Kari, Hamsten, Anders, Nicholson, George, Karpe, Fredrik, Dermitzakis, Emmanouil T., Lindgren, Cecilia M., McCarthy, Mark I., Froguel, Philippe, Kaakinen, Marika A., Lyssenko, Valeriya, Watanabe, Richard M., Ingelsson, Erik, Florez, Jose C., Dupuis, Josée, Barroso, Inês, Morris, Andrew P., and Prokopenko, Inga

Published
2021
Full Text
View/download PDF

35. Cholesteryl ester transfer protein (CETP) as a drug target for cardiovascular disease

Author

Schmidt, Amand F., Hunt, Nicholas B., Gordillo-Marañón, Maria, Charoen, Pimphen, Drenos, Fotios, Kivimaki, Mika, Lawlor, Deborah A., Giambartolomei, Claudia, Papacosta, Olia, Chaturvedi, Nishi, Bis, Joshua C., O’Donnell, Christopher J., Wannamethee, Goya, Wong, Andrew, Price, Jackie F., Hughes, Alun D., Gaunt, Tom R., Franceschini, Nora, Mook-Kanamori, Dennis O., Zwierzyna, Magdalena, Sofat, Reecha, Hingorani, Aroon D., and Finan, Chris

Published
2021
Full Text
View/download PDF

36. An informatics consult approach for generating clinical evidence for treatment decisions

Author

Lai, Alvina G., Chang, Wai Hoong, Parisinos, Constantinos A., Katsoulis, Michail, Blackburn, Ruth M., Shah, Anoop D., Nguyen, Vincent, Denaxas, Spiros, Davey Smith, George, Gaunt, Tom R., Nirantharakumar, Krishnarajah, Cox, Murray P., Forde, Donall, Asselbergs, Folkert W., Harris, Steve, Richardson, Sylvia, Sofat, Reecha, Dobson, Richard J. B., Hingorani, Aroon, Patel, Riyaz, Sterne, Jonathan, Banerjee, Amitava, Denniston, Alastair K., Ball, Simon, Sebire, Neil J., Shah, Nigam H., Foster, Graham R., Williams, Bryan, and Hemingway, Harry

Published
2021
Full Text
View/download PDF

37. Validation of lipid-related therapeutic targets for coronary heart disease prevention using human genetics

Author

Gordillo-Marañón, María, Zwierzyna, Magdalena, Charoen, Pimphen, Drenos, Fotios, Chopade, Sandesh, Shah, Tina, Engmann, Jorgen, Chaturvedi, Nishi, Papacosta, Olia, Wannamethee, Goya, Wong, Andrew, Sofat, Reecha, Kivimaki, Mika, Price, Jackie F., Hughes, Alun D., Gaunt, Tom R., Lawlor, Deborah A., Gaulton, Anna, Hingorani, Aroon D., Schmidt, Amand F., and Finan, Chris

Published
2021
Full Text
View/download PDF

41. Bayesian Test for Colocalisation Between Pairs of Genetic Association Studies Using Summary Statistics

Author

Giambartolomei, Claudia, Vukcevic, Damjan, Schadt, Eric E., Franke, Lude, Hingorani, Aroon D., Wallace, Chris, and Plagnol, Vincent

Subjects
Quantitative Biology - Genomics
Abstract

Genetic association studies, in particular the genome-wide association study design, have provided a wealth of novel insights into the aetiology of a wide range of human diseases and traits. The next challenge consists of understanding the molecular basis of these associations. The integration of multiple association datasets, including gene expression datasets, can contribute to this goal. We have developed a novel statistical methodology to assess whether two association signals are consistent with a shared causal variant. An application is the integration of disease scans with expression quantitative trait locus (eQTL) studies, but any pair of GWAS datasets can be integrated in this framework. We demonstrate the value of the approach by reanalysing a gene expression dataset in 966 liver samples with a published meta-analysis of lipid traits including >100, 000 individuals of European ancestry. Combining all lipid biomarkers, our reanalysis supported 29 out of 38 reported colocalisation results with eQTLs and identified 14 new colocalisation results, highlighting the value of a formal statistical test. In two cases of reported eQTL-lipid pairs (IFT172, TBKBP1) for which our analysis suggests that the eQTL pattern is not consistent with the lipid association, we identify alternative colocalisation results with GCKR and KPNB1, indicating that these genes are more likely to be causal in these genomic intervals. A key feature of the method is the ability to derive the output statistics from single SNP summary statistics, hence making it possible to perform systematic meta-analysis type comparisons across multiple GWAS datasets (http://coloc.cs.ucl.ac.uk/coloc/). Our methodology provides information about candidate causal genes in associated intervals and has direct implications for the understanding of complex diseases and the design of drugs to target disease pathways., Comment: Number of pages in main article: 25; Number of figures in main article: 6. To be published in Plos Genetics

Published
2013
Full Text
View/download PDF

42. 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
Full Text
View/download PDF

44. Systemic effects of periodontitis treatment in patients with type 2 diabetes: a 12 month, single-centre, investigator-masked, randomised trial

Author

Lomax, Alastair, Horvath, Attila, Zambon, Riccardo, Tay, Shiefung, Tatarakis, Nikos, Spratt, Dave, Kingston, Isabel, Parkar, Mohamed, Darbar, Ulpee, Patel, Kalpesh, Giedrys-Leeper, Elaine, Harrington, Zoe, Baynes, Kevin, Hughes, Francis, Gable, David, Patel, Pratik, Haria, Ankeet, Lessani, Michael, Moskal-Fitzpatrick, Donna, Curra', Maria Chiara, Hirani, Banbai, Niziolek, Kasia, Mellor, Tiffany, D'Aiuto, Francesco, Gkranias, Nikolaos, Bhowruth, Devina, Khan, Tauseef, Orlandi, Marco, Suvan, Jean, Masi, Stefano, Tsakos, Georgios, Hurel, Steve, Hingorani, Aroon D, Donos, Nikos, and Deanfield, John E

Published
2018
Full Text
View/download PDF

45. NMR metabolomic modelling of age and lifespan: a multi-cohort analysis

Author

Lau, Chung-Ho E, primary, Manou, Maria, additional, Markozannes, Georgios, additional, Ala-Korpela, Mika, additional, Ben-Shlomo, Yoav, additional, Chaturvedi, Nish, additional, Engmann, Jorgen, additional, Gentry-Maharaj, Aleksandra, additional, Herzig, Karl-Heinz, additional, Hingorani, Aroon, additional, Jarvelin, Marjo-Riitta, additional, Kahonen, Mika, additional, Kivimaki, Mika, additional, Lehtimaki, Terho, additional, Marttila, Saara, additional, Menon, Usha, additional, Munroe, Patricia B, additional, Palaniswamy, Saranya, additional, Providencia, Rui, additional, Raitakari, Olli, additional, Schmidt, Floriaan, additional, Sebert, Sylvain, additional, Wong, Andrew, additional, Vineis, Paolo, additional, Tzoulaki, Ioanna, additional, and Robinson, Oliver, additional

Published
2023
Full Text
View/download PDF

47. Ethnic disparities in progression rates for sight-threatening diabetic retinopathy in diabetic eye screening: a population-based retrospective cohort study

Author

Olvera-Barrios, Abraham, primary, Owen, Christopher G, additional, Anderson, John, additional, Warwick, Alasdair N, additional, Chambers, Ryan, additional, Bolter, Louis, additional, Wu, Yue, additional, Welikala, Roshan, additional, Fajtl, Jiri, additional, Barman, Sarah A, additional, Remagnino, Paolo, additional, Chew, Emily Y, additional, Ferris, Frederick L, additional, Hingorani, Aroon D, additional, Sofat, Reecha, additional, Lee, Aaron Y, additional, Egan, Catherine, additional, Tufail, Adnan, additional, and Rudnicka, Alicja R, additional

Published
2023
Full Text
View/download PDF

48. Two-year recall for people with no diabetic retinopathy: a multi-ethnic population-based retrospective cohort study using real-world data to quantify the effect

Author

Olvera-Barrios, Abraham, primary, Rudnicka, Alicja R, additional, Anderson, John, additional, Bolter, Louis, additional, Chambers, Ryan, additional, Warwick, Alasdair N, additional, Welikala, Roshan, additional, Fajtl, Jiri, additional, Barman, Sarah, additional, Remgnino, Paolo, additional, Wu, Yue, additional, Lee, Aaron Y, additional, Chew, Emily Y, additional, Ferris, Frederick L, additional, Hingorani, Aroon, additional, Sofat, Reecha, additional, A. Egan, Catherine, additional, Tufail, Adnan, additional, and Owen, Christopher G, additional

Published
2023
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results