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1. The Active Brains Digital Intervention to Reduce Cognitive Decline in Older Adults: Protocol for a Feasibility Randomized Controlled Trial

Author

Smith, Kirsten Ailsa, Bradbury, Katherine, Essery, Rosie, Pollet, Sebastien, Mowbray, Fiona, Slodkowska-Barabasz, Joanna, Denison-Day, James, Hayter, Victoria, Kelly, Jo, Somerville, Jane, Zhang, Jin, Grey, Elisabeth, Western, Max, Ferrey, Anne E, Krusche, Adele, Stuart, Beth, Mutrie, Nanette, Robinson, Sian, Yao, Guiqing Lily, Griffiths, Gareth, Robinson, Louise, Rossor, Martin, Gallacher, John, Griffin, Simon, Kendrick, Tony, Rathod, Shanaya, Gudgin, Bernard, Phillips, Rosemary, Stokes, Tom, Niven, John, Little, Paul, and Yardley, Lucy

Subjects
Medicine, Computer applications to medicine. Medical informatics, R858-859.7
Abstract

BackgroundIncreasing physical activity, improving diet, and performing brain training exercises are associated with reduced cognitive decline in older adults. ObjectiveIn this paper, we describe a feasibility trial of the Active Brains intervention, a web-based digital intervention developed to support older adults to make these 3 healthy behavior changes associated with improved cognitive health. The Active Brains trial is a randomized feasibility trial that will test how accessible, acceptable, and feasible the Active Brains intervention is and the effectiveness of the study procedures that we intend to use in the larger, main trial. MethodsIn the randomized controlled trial (RCT), we use a parallel design. We will be conducting the intervention with 2 populations recruited through GP practices (family practices) in England from 2018 to 2019: older adults with signs of cognitive decline and older adults without any cognitive decline. Trial participants were randomly allocated to 1 of 3 study groups: usual care, the Active Brains intervention, or the Active Brains website plus brief support from a trained coach (over the phone or by email). The main outcomes are performance on cognitive tasks, quality of life (using EuroQol-5D 5 level), Instrumental Activities of Daily Living, and diagnoses of dementia. Secondary outcomes (including depression, enablement, and health care costs) and process measures (including qualitative interviews with participants and supporters) will also be collected. The trial has been approved by the National Health Service Research Ethics Committee (reference 17/SC/0463). ResultsResults will be published in peer-reviewed journals, presented at conferences, and shared at public engagement events. Data collection was completed in May 2020, and the results will be reported in 2021. ConclusionsThe findings of this study will help us to identify and make important changes to the website, the support received, or the study procedures before we progress to our main randomized phase III trial. Trial RegistrationInternational Standard Randomized Controlled Trial Number 23758980; http://www.isrctn.com/ISRCTN23758980 International Registered Report Identifier (IRRID)DERR1-10.2196/18929

Published
2020
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5. Periodontitis and Outer Retinal Thickness: a Cross-Sectional Analysis of the United Kingdom Biobank Cohort

Author

Allen, Naomi, Aslam, Tariq, Atan, Denize, Balaskas, Konsantinos, Barman, Sarah A., Barrett, Jenny H., Bishop, Paul, Black, Graeme, Braithwaite, Tasanee, Carare, Roxana O., Chakravarthy, Usha, Chan, Michelle, Chua, Sharon Y.L., Day, Alexander, Desai, Parul, Dhillon, Bal, Dick, Andrew D., Doney, Alexander, Egan, Cathy, Ennis, Sarah, Foster, Paul, Fruttiger, Marcus, Gallacher, John E.J., Garway-Heath, David F., Gibson, Jane, Guggenheim, Jeremy A., Hammond, Chris J., Hardcastle, Alison, Harding, Simon P., Hogg, Ruth E., Hysi, Pirro, Keane, Pearse A., Khaw, Sir Peng T., Khawaja, Anthony P., Lascaratos, Gerassimos, Littlejohns, Thoams, Lotery, Andrew J., Luben, Robert, Luthert, Phil, Macgillivray, Tom, Mackie, Sarah, McGuinness, Bernadette, McKay, Gareth J., McKibbin, Martin, Moore, Tony, Morgan, James E., O’Sullivan, Eoin, Oram, Richard, Owen, Chris G., Patel, Praveen, Paterson, Euan, Peto, Tunde, Petzold, Axel, Rahi, Jugnoo S., Rudnikca, Alicja R., Sattar, Naveed, Self, Jay, Sergouniotis, Panagiotis, Sivaprasad, Sobha, Steel, David, Stratton, Irene, Strouthidis, Nicholas, Sudlow, Cathie, Sun, Zihan, Tapp, Robyn, Thomas, Dhanes, Trucco, Emanuele, Tufail, Adnan, Vitart, Veronique, Viswanathan, Ananth C., Weedon, Mike, Williams, Cathy, Williams, Katie, Woodside, Jayne V., Yates, Max M., Yip, Jennifer, Zheng, Yalin, Wagner, Siegfried K., Patel, Praveen J., Huemer, Josef, Khalid, Hagar, Stuart, Kelsey V., Chu, Colin J., Williamson, Dominic J., Struyven, Robbert R., Romero-Bascones, David, Foster, Paul J., Balaskas, Konstantinos, Cortina-Borja, Mario, Chapple, Iain, Dietrich, Thomas, and Denniston, Alastair K.

Published
2024
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8. Associations between unilateral amblyopia in childhood and cardiometabolic disorders in adult life: a cross-sectional and longitudinal analysis of the UK Biobank

Author

Allen, Naomi, Aslam, Tariq, Atan, Denize, Balaskas, Konstantinos, Barman, Sarah, Barrett, Jenny, Bishop, Paul, Black, Graeme, Braithwaite, Tasanee, Carare, Roxana, Chakravarthy, Usha, Chan, Michelle, Chua, Sharon, Day, Alexander, Desai, Parul, Dhillon, Bal, Dick, Andrew, Doney, Alexander, Egan, Cathy, Ennis, Sarah, Foster, Paul, Fruttiger, Marcus, Gallacher, John, Garway-heath, David (Ted), Gibson, Jane, Guggenheim, Jeremy, Hammond, Chris, Hardcastle, Alison, Harding, Simon, Hogg, Ruth, Hysi, Pirro, Keane, Pearse, Tee Khaw, Sir Peng, Khawaja, Anthony, Lascaratos, Gerassimos, Littlejohns, Thomas, Lotery, Andrew, Luben, Robert, Luthert, Phil, Macgillivray, Tom, Mackie, Sarah, Madhusudhan, Savita, Mcguinness, Bernadette, Mckay, Gareth, Mckibbin, Martin, Moore, Tony, Morgan, James, O'sullivan, Eoin, Oram, Richard, Owen, Chris, Patel, Praveen, Paterson, Euan, Peto, Tunde, Petzold, Axel, Pontikos, Nikolas, Rahi, Jugnoo, Rudnicka, Alicja, Sattar, Naveed, Self, Jay, Sergouniotis, Panagiotis, Sivaprasad, Sobha, Steel, David, Stratton, Irene, Strouthidis, Nicholas, Sudlow, Cathie, Sun, Zihan, Tapp, Robyn, Thomas, Dhanes, Trucco, Emanuele, Tufail, Adnan, Viswanathan, Ananth, Vitart, Veronique, Weedon, Mike, Williams, Katie, Williams, Cathy, Woodside, Jayne, Yates, Max, Zheng, Yalin, Thomas, Mervyn, Wagner, Siegfried Karl, Bountziouka, Vasiliki, and Rahi, Jugnoo Sangeeta

Published
2024
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10. Leveraging cross-platform data to improve automated hate speech detection

Author

Gallacher, John D

Subjects
Computer Science - Computation and Language
Abstract

Hate speech is increasingly prevalent online, and its negative outcomes include increased prejudice, extremism, and even offline hate crime. Automatic detection of online hate speech can help us to better understand these impacts. However, while the field has recently progressed through advances in natural language processing, challenges still remain. In particular, most existing approaches for hate speech detection focus on a single social media platform in isolation. This limits both the use of these models and their validity, as the nature of language varies from platform to platform. Here we propose a new cross-platform approach to detect hate speech which leverages multiple datasets and classification models from different platforms and trains a superlearner that can combine existing and novel training data to improve detection and increase model applicability. We demonstrate how this approach outperforms existing models, and achieves good performance when tested on messages from novel social media platforms not included in the original training data., Comment: 34 pages, 10 figures

Published
2021

11. Scars of life

Author

Baxter, Charlotte, Farrell, L. J., Canham, Maxine, Stewart, Mikayla, Maher, Orla, Gallacher, John, and Cook, Teagan

Published
2010

12. Estimating dose-response relationships for vitamin D with coronary heart disease, stroke, and all-cause mortality: observational and Mendelian randomisation analyses

Author

Sofianopoulou, Eleni, Kaptoge, Stephen K, Afzal, Shoaib, Jiang, Tao, Gill, Dipender, Gundersen, Thomas E, Bolton, Thomas R, Allara, Elias, Arnold, Matthew G, Mason, Amy M, Chung, Ryan, Pennells, Lisa A M, Shi, Fanchao, Sun, Luanluan, Willeit, Peter, Forouhi, Nita G, Langenberg, Claudia, Sharp, Stephen J, Panico, Salvatore, Engström, Gunnar, Melander, Olle, Tong, Tammy Y N, Perez-Cornago, Aurora, Norberg, Margareta, Johansson, Ingegerd, Katzke, Verena, Srour, Bernard, Sánchez, María José, Redondo-Sánchez, Daniel, Olsen, Anja, Dahm, Christina C, Overvad, Kim, Brustad, Magritt, Skeie, Guri, Moreno-Iribas, Conchi, Onland-Moret, N Charlotte, van der Schouw, Yvonne T, Tsilidis, Konstantinos K, Heath, Alicia K, Agnoli, Claudia, Krogh, Vittorio, de Boer, Ian H, Kobylecki, Camilla Jannie, Çolak, Yunus, Zittermann, Armin, Sundström, Johan, Welsh, Paul, Weiderpass, Elisabete, Aglago, Elom K, Ferrari, Pietro, Clarke, Robert, Boutron, Marie-Christine, Severi, Gianluca, MacDonald, Conor, Providencia, Rui, Masala, Giovanna, Zamora Ros, Raul, Boer, Jolanda, Verschuren, Wm Monique, Cawthon, Peggy, Schierbeck, Louise L, Cooper, Cyrus, Schulze, Matthias B, Bergmann, Manuela M, Hannemann, Anke, Kiechl, Stefan, Brenner, Hermann, van Schoor, Natasja M, Albertorio, Juan R, Sacerdote, Carlotta, Linneberg, Allan, Kårhus, LineL, Huerta, José María, Imaz, Liher, Joergensen, Christel, Ben-Shlomo, Yoav, Lundqvist, Annamari, Gallacher, John, Sattar, Naveed, Wood, Angela M, Wareham, Nicholas J, Nordestgaard, Børge G, Di Angelantonio, Emanuele, Danesh, John, Butterworth, Adam S, and Burgess, Stephen

Published
2024
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15. The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up

Author

Marinescu, Razvan V., Oxtoby, Neil P., Young, Alexandra L., Bron, Esther E., Toga, Arthur W., Weiner, Michael W., Barkhof, Frederik, Fox, Nick C., Eshaghi, Arman, Toni, Tina, Salaterski, Marcin, Lunina, Veronika, Ansart, Manon, Durrleman, Stanley, Lu, Pascal, Iddi, Samuel, Li, Dan, Thompson, Wesley K., Donohue, Michael C., Nahon, Aviv, Levy, Yarden, Halbersberg, Dan, Cohen, Mariya, Liao, Huiling, Li, Tengfei, Yu, Kaixian, Zhu, Hongtu, Tamez-Pena, Jose G., Ismail, Aya, Wood, Timothy, Bravo, Hector Corrada, Nguyen, Minh, Sun, Nanbo, Feng, Jiashi, Yeo, B. T. Thomas, Chen, Gang, Qi, Ke, Chen, Shiyang, Qiu, Deqiang, Buciuman, Ionut, Kelner, Alex, Pop, Raluca, Rimocea, Denisa, Ghazi, Mostafa M., Nielsen, Mads, Ourselin, Sebastien, Sorensen, Lauge, Venkatraghavan, Vikram, Liu, Keli, Rabe, Christina, Manser, Paul, Hill, Steven M., Howlett, James, Huang, Zhiyue, Kiddle, Steven, Mukherjee, Sach, Rouanet, Anais, Taschler, Bernd, Tom, Brian D. M., White, Simon R., Faux, Noel, Sedai, Suman, Oriol, Javier de Velasco, Clemente, Edgar E. V., Estrada, Karol, Aksman, Leon, Altmann, Andre, Stonnington, Cynthia M., Wang, Yalin, Wu, Jianfeng, Devadas, Vivek, Fourrier, Clementine, Raket, Lars Lau, Sotiras, Aristeidis, Erus, Guray, Doshi, Jimit, Davatzikos, Christos, Vogel, Jacob, Doyle, Andrew, Tam, Angela, Diaz-Papkovich, Alex, Jammeh, Emmanuel, Koval, Igor, Moore, Paul, Lyons, Terry J., Gallacher, John, Tohka, Jussi, Ciszek, Robert, Jedynak, Bruno, Pandya, Kruti, Bilgel, Murat, Engels, William, Cole, Joseph, Golland, Polina, Klein, Stefan, and Alexander, Daniel C.

Subjects
Quantitative Biology - Populations and Evolution, Statistics - Applications
Abstract

We present the findings of "The Alzheimer's Disease Prediction Of Longitudinal Evolution" (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international teams at predicting the future trajectory of 219 individuals at risk of Alzheimer's disease. Challenge participants were required to make a prediction, for each month of a 5-year future time period, of three key outcomes: clinical diagnosis, Alzheimer's Disease Assessment Scale Cognitive Subdomain (ADAS-Cog13), and total volume of the ventricles. The methods used by challenge participants included multivariate linear regression, machine learning methods such as support vector machines and deep neural networks, as well as disease progression models. No single submission was best at predicting all three outcomes. For clinical diagnosis and ventricle volume prediction, the best algorithms strongly outperform simple baselines in predictive ability. However, for ADAS-Cog13 no single submitted prediction method was significantly better than random guesswork. Two ensemble methods based on taking the mean and median over all predictions, obtained top scores on almost all tasks. Better than average performance at diagnosis prediction was generally associated with the additional inclusion of features from cerebrospinal fluid (CSF) samples and diffusion tensor imaging (DTI). On the other hand, better performance at ventricle volume prediction was associated with inclusion of summary statistics, such as the slope or maxima/minima of biomarkers. TADPOLE's unique results suggest that current prediction algorithms provide sufficient accuracy to exploit biomarkers related to clinical diagnosis and ventricle volume, for cohort refinement in clinical trials for Alzheimer's disease. However, results call into question the usage of cognitive test scores for patient selection and as a primary endpoint in clinical trials., Comment: Presents final results of the TADPOLE competition. 60 pages, 7 tables, 14 figures

Published
2020

16. Online intergroup conflict : how the dynamics of online communication drive extremism and violence between groups

Author

Gallacher, John David, Heerdink, Marc, Bright, Jonathan, and Wright, Joss

Subjects
303.48, Psychology, Radicalism, Conflict (Psychology), Internet, Terrorism
Abstract

In the early days of the Internet, the social web was suggested to hold the power to reduce conflict, prejudice, and discrimination, by promoting connection between those with diverse backgrounds and experiences. However, this optimistic view has not materialised. Instead, evidence shows that Western societies are increasingly polarised and divided, and social media has been particularly implicated with the promotion of violent extremism and the spread of hate speech. The exact role social media plays in driving these processes remains poorly understood. This thesis investigates the mechanisms through which social media may foster and exacerbate intergroup conflict both online and offline, and explores different intergroup and intragroup dynamics which may drive this trend. This work firstly investigates the prevalence and impact of online communication between opposing groups on offline intergroup relations, demonstrating how intergroup communication does frequently occur online, but can be confrontational, hostile, and fails to improve intergroup relations, instead predicting future offline violence. Following this, we then develop a novel approach to detect online hate speech by leveraging datasets from multiple social media platforms in order to train natural language machine learning classifiers. We use these classifiers to explore the influence of extreme outgroup denigration in ingroup discussions on fringe social media platforms popular with the far-right. We demonstrate how exposure to online hate plays a role in shaping individual radicalisation trajectories, increasing the likelihood of offline hate crimes, and driving mutual radicalisation with opposing groups. Finally, we explore the effect of hostile manipulation from state actors on the dynamics of online conversations, presenting novel techniques to reveal how manipulation of these discussions increases the polarisation of online conversations from genuine users. Together these findings shed light on the mechanisms by which the Internet promotes intergroup conflict, extremism, and violence, providing new insight into initial steps that could be used to counter these effects.

Published
2021

17. Trajectories in chronic disease accrual and mortality across the lifespan in Wales, UK (2005–2019), by area deprivation profile: linked electronic health records cohort study on 965,905 individuals

Author

Lyons, Jane, Akbari, Ashley, Abrams, Keith R., Azcoaga Lorenzo, Amaya, Ba Dhafari, Thamer, Chess, James, Denaxas, Spiros, Fry, Richard, Gale, Chris P., Gallacher, John, Griffiths, Lucy J., Guthrie, Bruce, Hall, Marlous, Jalali-najafabadi, Farideh, John, Ann, MacRae, Clare, McCowan, Colin, Peek, Niels, O’Reilly, Dermot, Rafferty, James, Lyons, Ronan A., and Owen, Rhiannon K.

Published
2023
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18. The Association of Alcohol Consumption with Glaucoma and Related Traits: Findings from the UK Biobank

Author

Aschard, Hugues, Chia, Mark, Chua, Sharon, Do, Ron, Foster, Paul, Kang, Jae, Kastner, Alan, Khawaja, Anthony, Kim, Jihye, Lentjes, Marleen, Luben, Robert, Madjedi, Kian, Montesano, Giovanni, Pasquale, Louis, Stuart, Kelsey, Warwick, Alasdair, Wiggs, Janey, Allen, Naomi, Aslam, Tariq, Atan, Denize, Barman, Sarah, Barrett, Jenny, Bishop, Paul, Black, Graeme, Braithwaite, Tasanee, Carare, Roxana, Chakravarthy, Usha, Chan, Michelle, Day, Alexander, Desai, Parul, Dhillon, Bal, Dick, Andrew, Doney, Alexander, Egan, Cathy, Ennis, Sarah, Fruttiger, Marcus, Gallacher, John, Garway-Heath, David (Ted), Gibson, Jane, Guggenheim, Jeremy, Hammond, Chris, Hardcastle, Alison, Harding, Simon, Hogg, Ruth, Hysi, Pirro, Keane, Pearse, Khaw, Peng Tee, Lascaratos, Gerassimos, Littlejohns, Thomas, Lotery, Andrew, Luthert, Phil, MacGillivray, Tom, Mackie, Sarah, McGuinness, Bernadette, McKay, Gareth, McKibbin, Martin, Moore, Tony, Morgan, James, O'Sullivan, Eoin, Oram, Richard, Owen, Chris, Patel, Praveen, Paterson, Euan, Peto, Tunde, Petzold, Axel, Pontikos, Nikolas, Rahi, Jugnoo, Rudnicka, Alicja, Sattar, Naveed, Self, Jay, Sergouniotis, Panagiotis, Sivaprasad, Sobha, Steel, David, Stratton, Irene, Strouthidis, Nicholas, Sudlow, Cathie, Sun, Zihan, Tapp, Robyn, Thomas, Dhanes, Trucco, Emanuele, Tufail, Adnan, Viswanathan, Ananth, Vitart, Veronique, Weedon, Mike, Williams, Katie, Williams, Cathy, Woodside, Jayne, Yates, Max, Yip, Jennifer, Zheng, Yalin, Aung, Tin, Burdon, Kathryn, Chen, Li, Cheng, Ching-Yu, Craig, Jamie, Cree, Angela, de Vries, Victor, Driessen, Sjoerd, Fingert, John, Gharahkhani, Puya, Hammond, Christopher, Hayward, Caroline, Hewitt, Alex, Jansonius, Nomdo, Jonansson, Fridbert, Jonas, Jost, Kass, Michael, Khor, Chiea, Klaver, Caroline, Koh, Jacyline, MacGregor, Stuart, Mackey, David, Mitchell, Paul, Pang, Calvin, Pasutto, Francesca, Pfeiffer, Norbert, Polašek, Ozren, Ramdas, Wishal, Schuster, Alexander, Segrè, Ayellet, Stefansson, Einer, Stefánsson, Kári, Thorleifsson, Gudmar, Thorsteinsdottir, Unnur, van Duijn, Cornelia, Vergroesen, Joëlle, Vithana, Eranga, Wilson, James, Wojciechowski, Robert, Wong, Tien, Young, Terri, Stuart, Kelsey V., Luben, Robert N., Warwick, Alasdair N., Madjedi, Kian M., Patel, Praveen J., Biradar, Mahantesh I., Chia, Mark A., Pasquale, Louis R., Wiggs, Janey L., Kang, Jae H., Tran, Jessica H., Lentjes, Marleen A.H., Foster, Paul J., and Khawaja, Anthony P.

Published
2023
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20. A new polygenic score for refractive error improves detection of children at risk of high myopia but not the prediction of those at risk of myopic macular degeneration

Author

Bailey-Wilson, Joan E., Baird, Paul N., Barathi, Veluchamy A., Biino, Ginevra, Burdon, Kathryn P., Campbell, Harry, Chen, Li Jia, Cheng, Ching-Yu, Chew, Emily Y., Craig, Jamie E., Deangelis, Margaret M., Delcourt, Cécile, Ding, Xiaohu, Fan, Qiao, Fossarello, Maurizio, Foster, Paul J., Gharahkhani, Puya, Guggenheim, Jeremy A., Guo, Xiaobo, Haarman, Annechien E.G., Haller, Toomas, Hammond, Christopher J., Han, Xikun, Hayward, Caroline, He, Mingguang, Hewitt, Alex W., Hoang, Quan, Hysi, Pirro G., Iglesias, Adriana I., Igo, Robert P., Iyengar, Sudha K., Jonas, Jost B., Kähönen, Mika, Kaprio, Jaakko, Khawaja, Anthony P., Klein, Barbara E., Lass, Jonathan H., Lee, Kris, Lehtimäki, Terho, Lewis, Deyana, Li, Qing, Li, Shi-Ming, Lyytikäinen, Leo-Pekka, MacGregor, Stuart, Mackey, David A., Martin, Nicholas G., Meguro, Akira, Metspalu, Andres, Middlebrooks, Candace, Miyake, Masahiro, Mizuki, Nobuhisa, Musolf, Anthony, Nickels, Stefan, Oexle, Konrad, Pang, Chi Pui, Pärssinen, Olavi, Paterson, Andrew D., Pfeiffer, Norbert, Polasek, Ozren, Rahi, Jugnoo S., Raitakari, Olli, Rudan, Igor, Sahebjada, Srujana, Saw, Seang-Mei, Simpson, Claire L., Stambolian, Dwight, Tai, E-Shyong, Tedja, Milly S., Tideman, J. Willem L., Tsujikawa, Akitaka, van Duijn, Cornelia M., Verhoeven, Virginie J.M., Vitart, Veronique, Wang, Ningli, Wang, Ya Xing, Wedenoja, Juho, Wei, Wen Bin, Williams, Cathy, Williams, Katie M., Wilson, James F., Wojciechowski, Robert, Yam, Jason C.S., Yamashiro, Kenji, Yap, Maurice K.H., Yazar, Seyhan, Yip, Shea Ping, Young, Terri L., Zhou, Xiangtian, Allen, Naomi, Aslam, Tariq, Atan, Denize, Barman, Sarah, Barrett, Jenny, Bishop, Paul, Black, Graeme, Bunce, Catey, Carare, Roxana, Chakravarthy, Usha, Chan, Michelle, Chua, Sharon, Cipriani, Valentina, Day, Alexander, Desai, Parul, Dhillon, Bal, Dick, Andrew, Doney, Alexander, Egan, Cathy, Ennis, Sarah, Foster, Paul, Fruttiger, Marcus, Gallacher, John, Garway-Heath, David, Gibson, Jane, Gore, Dan, Guggenheim, Jeremy, Hammond, Chris, Hardcastle, Alison, Harding, Simon, Hogg, Ruth, Hysi, Pirro, Keane, Pearse A., Khaw, Peng Tee, Khawaja, Anthony, Lascaratos, Gerassimos, Littlejohns, Thomas, Lotery, Andrew, Luthert, Phil, MacGillivray, Tom, Mackie, Sarah, McGuinness, Bernadette, McKay, Gareth, McKibbin, Martin, Mitry, Danny, Moore, Tony, Morgan, James, Muthy, Zaynah, O'Sullivan, Eoin, Owen, Chris, Patel, Praveen, Paterson, Euan, Peto, Tunde, Petzold, Axel, Pontikos, Nikolas, Rahi, Jugnoo, Rudnicka, Alicja, Self, Jay, Sergouniotis, Panagiotis, Sivaprasad, Sobha, Steel, David, Stratton, Irene, Strouthidis, Nicholas, Sudlow, Cathie, Tapp, Robyn, Thaung, Caroline, Thomas, Dhanes, Trucco, Emanuele, Tufail, Adnan, Vernon, Stephen, Viswanathan, Ananth, Williams, Katie, Woodside, Jayne, Yates, Max, Yip, Jennifer, Zheng, Yalin, Clark, Rosie, Lee, Samantha Sze-Yee, Du, Ran, Wang, Yining, Kneepkens, Sander C.M., Charng, Jason, Huang, Yu, Hunter, Michael L., Jiang, Chen, Tideman, J.Willem L., Melles, Ronald B., Klaver, Caroline C.W., Choquet, Hélène, and Ohno-Matsui, Kyoko

Published
2023
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21. 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

22. A multi-disciplinary commentary on preclinical research to investigate vascular contributions to dementia

Author

Sri, Sarmi, Greenstein, Adam, Granata, Alessandra, Collcutt, Alex, Jochems, Angela C C, McColl, Barry W, Castro, Blanca Díaz, Webber, Caleb, Reyes, Carmen Arteaga, Hall, Catherine, Lawrence, Catherine B, Hawkes, Cheryl, Pegasiou-Davies, Chrysia-Maria, Gibson, Claire, Crawford, Colin L, Smith, Colin, Vivien, Denis, McLean, Fiona H, Wiseman, Frances, Brezzo, Gaia, Lalli, Giovanna, Pritchard, Harry A T, Markus, Hugh S, Bravo-Ferrer, Isabel, Taylor, Jade, Leiper, James, Berwick, Jason, Gan, Jian, Gallacher, John, Moss, Jonathan, Goense, Jozien, McMullan, Letitia, Work, Lorraine, Evans, Lowri, Stringer, Michael S, Ashford, MLJ, Abulfadl, Mohamed, Conlon, Nina, Malhotra, Paresh, Bath, Philip, Canter, Rebecca, Brown, Rosalind, Ince, Selvi, Anderle, Silvia, Young, Simon, Quick, Sophie, Szymkowiak, Stefan, Hill, Steve, Allan, Stuart, Wang, Tao, Quinn, Terry, Procter, Tessa, Farr, Tracy D, Zhao, Xiangjun, Yang, Zhiyuan, Hainsworth, Atticus H, and Wardlaw, Joanna M

Published
2023
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23. Random forest prediction of Alzheimer's disease using pairwise selection from time series data

Author

Moore, Paul, Lyons, Terry, and Gallacher, John

Subjects
Quantitative Biology - Quantitative Methods, Statistics - Applications, 62M10
Abstract

Time-dependent data collected in studies of Alzheimer's disease usually has missing and irregularly sampled data points. For this reason time series methods which assume regular sampling cannot be applied directly to the data without a pre-processing step. In this paper we use a machine learning method to learn the relationship between pairs of data points at different time separations. The input vector comprises a summary of the time series history and includes both demographic and non-time varying variables such as genetic data. The dataset used is from the 2017 TADPOLE grand challenge which aims to predict the onset of Alzheimer's disease using including demographic, physical and cognitive data. The challenge is a three-fold diagnosis classification into AD, MCI and control groups, the prediction of ADAS-13 score and the normalised ventricle volume. While the competition proceeds, forecasting methods may be compared using a leaderboard dataset selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and with standard metrics for measuring accuracy. For diagnosis, we find an mAUC of 0.82, and a classification accuracy of 0.73. The results show that the method is effective and comparable with other methods., Comment: 6 pages, 1 figure, 6 tables

Published
2018
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24. Junk News on Military Affairs and National Security: Social Media Disinformation Campaigns Against US Military Personnel and Veterans

Author

Gallacher, John D., Barash, Vlad, Howard, Philip N., and Kelly, John

Subjects
Computer Science - Social and Information Networks
Abstract

Social media provides political news and information for both active duty military personnel and veterans. We analyze the subgroups of Twitter and Facebook users who spend time consuming junk news from websites that target US military personnel and veterans with conspiracy theories, misinformation, and other forms of junk news about military affairs and national security issues. (1) Over Twitter we find that there are significant and persistent interactions between current and former military personnel and a broad network of extremist, Russia-focused, and international conspiracy subgroups. (2) Over Facebook, we find significant and persistent interactions between public pages for military and veterans and subgroups dedicated to political conspiracy, and both sides of the political spectrum. (3) Over Facebook, the users who are most interested in conspiracy theories and the political right seem to be distributing the most junk news, whereas users who are either in the military or are veterans are among the most sophisticated news consumers, and share very little junk news through the network., Comment: Data Memo

Published
2018

26. Associations between unilateral amblyopia in childhood and cardiometabolic disorders in adult life: a cross-sectional and longitudinal analysis of the UK Biobank

Author

Wagner, Siegfried Karl, primary, Bountziouka, Vasiliki, additional, Hysi, Pirro, additional, Rahi, Jugnoo Sangeeta, additional, Allen, Naomi, additional, Aslam, Tariq, additional, Atan, Denize, additional, Balaskas, Konstantinos, additional, Barman, Sarah, additional, Barrett, Jenny, additional, Bishop, Paul, additional, Black, Graeme, additional, Braithwaite, Tasanee, additional, Carare, Roxana, additional, Chakravarthy, Usha, additional, Chan, Michelle, additional, Chua, Sharon, additional, Day, Alexander, additional, Desai, Parul, additional, Dhillon, Bal, additional, Dick, Andrew, additional, Doney, Alexander, additional, Egan, Cathy, additional, Ennis, Sarah, additional, Foster, Paul, additional, Fruttiger, Marcus, additional, Gallacher, John, additional, Garway-heath, David (Ted), additional, Gibson, Jane, additional, Guggenheim, Jeremy, additional, Hammond, Chris, additional, Hardcastle, Alison, additional, Harding, Simon, additional, Hogg, Ruth, additional, Keane, Pearse, additional, Tee Khaw, Sir Peng, additional, Khawaja, Anthony, additional, Lascaratos, Gerassimos, additional, Littlejohns, Thomas, additional, Lotery, Andrew, additional, Luben, Robert, additional, Luthert, Phil, additional, Macgillivray, Tom, additional, Mackie, Sarah, additional, Madhusudhan, Savita, additional, Mcguinness, Bernadette, additional, Mckay, Gareth, additional, Mckibbin, Martin, additional, Moore, Tony, additional, Morgan, James, additional, O'sullivan, Eoin, additional, Oram, Richard, additional, Owen, Chris, additional, Patel, Praveen, additional, Paterson, Euan, additional, Peto, Tunde, additional, Petzold, Axel, additional, Pontikos, Nikolas, additional, Rahi, Jugnoo, additional, Rudnicka, Alicja, additional, Sattar, Naveed, additional, Self, Jay, additional, Sergouniotis, Panagiotis, additional, Sivaprasad, Sobha, additional, Steel, David, additional, Stratton, Irene, additional, Strouthidis, Nicholas, additional, Sudlow, Cathie, additional, Sun, Zihan, additional, Tapp, Robyn, additional, Thomas, Dhanes, additional, Trucco, Emanuele, additional, Tufail, Adnan, additional, Viswanathan, Ananth, additional, Vitart, Veronique, additional, Weedon, Mike, additional, Williams, Katie, additional, Williams, Cathy, additional, Woodside, Jayne, additional, Yates, Max, additional, Zheng, Yalin, additional, and Thomas, Mervyn, additional

Published
2024
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30. The Dementias Platform UK (DPUK) Data Portal

Author

Bauermeister, Sarah, Orton, Christopher, Thompson, Simon, Barker, Roger A., Bauermeister, Joshua R., Ben-Shlomo, Yoav, Brayne, Carol, Burn, David, Campbell, Archie, Calvin, Catherine, Chandran, Siddharthan, Chaturvedi, Nishi, Chêne, Geneviève, Chessell, Iain P., Corbett, Anne, Davis, Daniel H. J., Denis, Mike, Dufouil, Carole, Elliott, Paul, Fox, Nick, Hill, Derek, Hofer, Scott M., Hu, Michele T., Jindra, Christoph, Kee, Frank, Kim, Chi-Hun, Kim, Changsoo, Kivimaki, Mika, Koychev, Ivan, Lawson, Rachael A., Linden, Gerry J., Lyons, Ronan A., Mackay, Clare, Matthews, Paul M., McGuiness, Bernadette, Middleton, Lefkos, Moody, Catherine, Moore, Katrina, Na, Duk L., O’Brien, John T., Ourselin, Sebastien, Paranjothy, Shantini, Park, Ki-Soo, Porteous, David J., Richards, Marcus, Ritchie, Craig W., Rohrer, Jonathan D., Rossor, Martin N., Rowe, James B., Scahill, Rachael, Schnier, Christian, Schott, Jonathan M., Seo, Sang W., South, Matthew, Steptoe, Matthew, Tabrizi, Sarah J., Tales, Andrea, Tillin, Therese, Timpson, Nicholas J., Toga, Arthur W., Visser, Pieter-Jelle, Wade-Martins, Richard, Wilkinson, Tim, Williams, Julie, Wong, Andrew, and Gallacher, John E. J.

Published
2020

31. Semantic Harmonization of Alzheimer's Disease Datasets Using AD-Mapper.

Author

Wegner, Philipp, Balabin, Helena, Ay, Mehmet Can, Bauermeister, Sarah, Killin, Lewis, Gallacher, John, Hofmann-Apitius, Martin, and Salimi, Yasamin

Subjects
ALZHEIMER'S disease
Abstract

Background: Despite numerous past endeavors for the semantic harmonization of Alzheimer's disease (AD) cohort studies, an automatic tool has yet to be developed. Objective: As cohort studies form the basis of data-driven analysis, harmonizing them is crucial for cross-cohort analysis. We aimed to accelerate this task by constructing an automatic harmonization tool. Methods: We created a common data model (CDM) through cross-mapping data from 20 cohorts, three CDMs, and ontology terms, which was then used to fine-tune a BioBERT model. Finally, we evaluated the model using three previously unseen cohorts and compared its performance to a string-matching baseline model. Results: Here, we present our AD-Mapper interface for automatic harmonization of AD cohort studies, which outperformed a string-matching baseline on previously unseen cohort studies. We showcase our CDM comprising 1218 unique variables. Conclusion: AD-Mapper leverages semantic similarities in naming conventions across cohorts to improve mapping performance. [ABSTRACT FROM AUTHOR]

Published
2024
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32. Dementias Platform UK: Bringing genetics into life.

Author

Leonenko, Ganna, Bauermeister, Sarah, Ghanti, Dipanwita, Stevenson‐Hoare, Joshua, Simmonds, Emily, Brookes, Keeley, Morgan, Kevin, Chaturvedi, Nishi, Elliott, Paul, Thomas, Alan, Wareham, Nicholas, Gallacher, John, and Escott‐Price, Valentina

Abstract

INTRODUCTION: The Dementias Platform UK (DPUK) Data Portal is a data repository bringing together a wide range of cohorts. Neurodegenerative dementias are a group of diseases with highly heterogeneous pathology and an overlapping genetic component that is poorly understood. The DPUK collection of independent cohorts can facilitate research in neurodegeneration by combining their genetic and phenotypic data. METHODS: For genetic data processing, pipelines were generated to perform quality control analysis, genetic imputation, and polygenic risk score (PRS) derivation with six genome‐wide association studies of neurodegenerative diseases. Pipelines were applied to five cohorts. DISCUSSION: The data processing pipelines, research‐ready imputed genetic data, and PRS scores are now available on the DPUK platform and can be accessed upon request though the DPUK application process. Harmonizing genome‐wide data for multiple datasets increases scientific opportunity and allows the wider research community to access and process data at scale and pace. [ABSTRACT FROM AUTHOR]

Published
2024
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33. Prospective study design and data analysis in UK Biobank

Author

Allen, Naomi E., primary, Lacey, Ben, additional, Lawlor, Deborah A., additional, Pell, Jill P., additional, Gallacher, John, additional, Smeeth, Liam, additional, Elliott, Paul, additional, Matthews, Paul M., additional, Lyons, Ronan A., additional, Whetton, Anthony D., additional, Lucassen, Anneke, additional, Hurles, Matthew E., additional, Chapman, Michael, additional, Roddam, Andrew W., additional, Fitzpatrick, Natalie K., additional, Hansell, Anna L., additional, Hardy, Rebecca, additional, Marioni, Riccardo E., additional, O’Donnell, Valerie B., additional, Williams, Julie, additional, Lindgren, Cecilia M., additional, Effingham, Mark, additional, Sellors, Jonathan, additional, Danesh, John, additional, and Collins, Rory, additional

Published
2024
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34. Periodontitis and outer retinal thickness: A cross-sectional analysis of the UK Biobank cohort

Author

Wagner, Siegfried K., primary, Patel, Praveen J., additional, Huemer, Josef, additional, Khalid, Hagar, additional, Stuart, Kelsey V., additional, Chu, Colin J., additional, Williamson, Dominic J., additional, Struyven, Robbert R., additional, Romero-Bascones, David, additional, Foster, Paul J., additional, Khawaja, Anthony P., additional, Petzold, Axel, additional, Balaskas, Konstantinos, additional, Cortina-Borja, Mario, additional, Chapple, Iain, additional, Dietrich, Thomas, additional, Rahi, Jugnoo S., additional, Denniston, Alastair K., additional, Keane, Pearse A., additional, Allen, Naomi, additional, Aslam, Tariq, additional, Atan, Denize, additional, Balaskas, Konsantinos, additional, Barman, Sarah A., additional, Barrett, Jenny H., additional, Bishop, Paul, additional, Black, Graeme, additional, Braithwaite, Tasanee, additional, Carare, Roxana O., additional, Chakravarthy, Usha, additional, Chan, Michelle, additional, Chua, Sharon Y.L., additional, Day, Alexander, additional, Desai, Parul, additional, Dhillon, Bal, additional, Dick, Andrew D., additional, Doney, Alexander, additional, Egan, Cathy, additional, Ennis, Sarah, additional, Foster, Paul, additional, Fruttiger, Marcus, additional, Gallacher, John E.J., additional, Garway-Heath, David F., additional, Gibson, Jane, additional, Guggenheim, Jeremy A., additional, Hammond, Chris J., additional, Hardcastle, Alison, additional, Harding, Simon P., additional, Hogg, Ruth E., additional, Hysi, Pirro, additional, Khaw, Sir Peng T., additional, Lascaratos, Gerassimos, additional, Littlejohns, Thoams, additional, Lotery, Andrew J., additional, Luben, Robert, additional, Luthert, Phil, additional, Macgillivray, Tom, additional, Mackie, Sarah, additional, McGuinness, Bernadette, additional, McKay, Gareth J., additional, McKibbin, Martin, additional, Moore, Tony, additional, Morgan, James E., additional, O’Sullivan, Eoin, additional, Oram, Richard, additional, Owen, Chris G., additional, Patel, Praveen, additional, Paterson, Euan, additional, Peto, Tunde, additional, Rudnikca, Alicja R., additional, Sattar, Naveed, additional, Self, Jay, additional, Sergouniotis, Panagiotis, additional, Sivaprasad, Sobha, additional, Steel, David, additional, Stratton, Irene, additional, Strouthidis, Nicholas, additional, Sudlow, Cathie, additional, Sun, Zihan, additional, Tapp, Robyn, additional, Thomas, Dhanes, additional, Trucco, Emanuele, additional, Tufail, Adnan, additional, Vitart, Veronique, additional, Viswanathan, Ananth C., additional, Weedon, Mike, additional, Williams, Cathy, additional, Williams, Katie, additional, Woodside, Jayne V., additional, Yates, MaxM., additional, Yip, Jennifer, additional, and Zheng, Yalin, additional

Published
2024
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43. HMG-coenzyme A reductase inhibition, type 2 diabetes, and bodyweight: evidence from genetic analysis and randomised trials

Author

Swerdlow, Daniel I, Preiss, David, Kuchenbaecker, Karoline B, Holmes, Michael V, Engmann, Jorgen EL, Shah, Tina, Sofat, Reecha, Stender, Stefan, Johnson, Paul CD, Scott, Robert A, Leusink, Maarten, Verweij, Niek, Sharp, Stephen J, Guo, Yiran, Giambartolomei, Claudia, Chung, Christina, Peasey, Anne, Amuzu, Antoinette, Li, KaWah, Palmen, Jutta, Howard, Philip, Cooper, Jackie A, Drenos, Fotios, Li, Yun R, Lowe, Gordon, Gallacher, John, Stewart, Marlene CW, Tzoulaki, Ioanna, Buxbaum, Sarah G, van der A, Daphne L, Forouhi, Nita G, Onland-Moret, N Charlotte, van der Schouw, Yvonne T, Schnabel, Renate B, Hubacek, Jaroslav A, Kubinova, Ruzena, Baceviciene, Migle, Tamosiunas, Abdonas, Pajak, Andrzej, Topor-Madry, Romanvan, Stepaniak, Urszula, Malyutina, Sofia, Baldassarre, Damiano, Sennblad, Bengt, Tremoli, Elena, de Faire, Ulf, Veglia, Fabrizio, Ford, Ian, Jukema, J Wouter, Westendorp, Rudi GJ, de Borst, Gert Jan, de Jong, Pim A, Algra, Ale, Spiering, Wilko, der Zee, Anke H Maitland-van, Klungel, Olaf H, de Boer, Anthonius, Doevendans, Pieter A, Eaton, Charles B, Robinson, Jennifer G, Duggan, David, DIAGRAM Consortium, MAGIC Consortium, Kjekshus, John, Downs, John R, Gotto, Antonio M, Keech, Anthony C, Marchioli, Roberto, Tognoni, Gianni, Sever, Peter S, Poulter, Neil R, Waters, David D, Pedersen, Terje R, Amarenco, Pierre, Nakamura, Haruo, McMurray, John JV, Lewsey, James D, Chasman, Daniel I, Ridker, Paul M, Maggioni, Aldo P, Tavazzi, Luigi, Ray, Kausik K, Seshasai, Sreenivasa Rao Kondapally, Manson, JoAnn E, Price, Jackie F, Whincup, Peter H, Morris, Richard W, Lawlor, Debbie A, Smith, George Davey, Ben-Shlomo, Yoav, Schreiner, Pamela J, Fornage, Myriam, Siscovick, David S, Cushman, Mary, Kumari, Meena, Wareham, Nick J, Verschuren, WM Monique, Redline, Susan, Patel, Sanjay R, Whittaker, John C, and Hamsten, Anders

Subjects
Epidemiology, Biomedical and Clinical Sciences, Health Sciences, Clinical Trials and Supportive Activities, Clinical Research, Genetics, Nutrition, Diabetes, 6.1 Pharmaceuticals, Evaluation of treatments and therapeutic interventions, Metabolic and endocrine, Aged, Body Mass Index, Body Weight, Cholesterol, HDL, Cholesterol, LDL, Diabetes Mellitus, Type 2, Female, Genetic Testing, Humans, Hydroxymethylglutaryl CoA Reductases, Hydroxymethylglutaryl-CoA Reductase Inhibitors, Male, Middle Aged, Polymorphism, Single Nucleotide, Randomized Controlled Trials as Topic, Risk Factors, DIAGRAM Consortium, MAGIC Consortium, InterAct Consortium, Medical and Health Sciences, General & Internal Medicine, Biomedical and clinical sciences, Health sciences
Abstract

BackgroundStatins increase the risk of new-onset type 2 diabetes mellitus. We aimed to assess whether this increase in risk is a consequence of inhibition of 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR), the intended drug target.MethodsWe used single nucleotide polymorphisms in the HMGCR gene, rs17238484 (for the main analysis) and rs12916 (for a subsidiary analysis) as proxies for HMGCR inhibition by statins. We examined associations of these variants with plasma lipid, glucose, and insulin concentrations; bodyweight; waist circumference; and prevalent and incident type 2 diabetes. Study-specific effect estimates per copy of each LDL-lowering allele were pooled by meta-analysis. These findings were compared with a meta-analysis of new-onset type 2 diabetes and bodyweight change data from randomised trials of statin drugs. The effects of statins in each randomised trial were assessed using meta-analysis.FindingsData were available for up to 223 463 individuals from 43 genetic studies. Each additional rs17238484-G allele was associated with a mean 0·06 mmol/L (95% CI 0·05-0·07) lower LDL cholesterol and higher body weight (0·30 kg, 0·18-0·43), waist circumference (0·32 cm, 0·16-0·47), plasma insulin concentration (1·62%, 0·53-2·72), and plasma glucose concentration (0·23%, 0·02-0·44). The rs12916 SNP had similar effects on LDL cholesterol, bodyweight, and waist circumference. The rs17238484-G allele seemed to be associated with higher risk of type 2 diabetes (odds ratio [OR] per allele 1·02, 95% CI 1·00-1·05); the rs12916-T allele association was consistent (1·06, 1·03-1·09). In 129 170 individuals in randomised trials, statins lowered LDL cholesterol by 0·92 mmol/L (95% CI 0·18-1·67) at 1-year of follow-up, increased bodyweight by 0·24 kg (95% CI 0·10-0·38 in all trials; 0·33 kg, 95% CI 0·24-0·42 in placebo or standard care controlled trials and -0·15 kg, 95% CI -0·39 to 0·08 in intensive-dose vs moderate-dose trials) at a mean of 4·2 years (range 1·9-6·7) of follow-up, and increased the odds of new-onset type 2 diabetes (OR 1·12, 95% CI 1·06-1·18 in all trials; 1·11, 95% CI 1·03-1·20 in placebo or standard care controlled trials and 1·12, 95% CI 1·04-1·22 in intensive-dose vs moderate dose trials).InterpretationThe increased risk of type 2 diabetes noted with statins is at least partially explained by HMGCR inhibition.FundingThe funding sources are cited at the end of the paper.

Published
2015

44. Longer-term efficiency and safety of increasing the frequency of whole blood donation (INTERVAL): extension study of a randomised trial of 20 757 blood donors

Author

Kaptoge, Stephen, Di Angelantonio, Emanuele, Moore, Carmel, Walker, Matthew, Armitage, Jane, Ouwehand, Willem H, Roberts, David J, Danesh, John, Thompson, Simon G, Donovan, Jenny, Ford, Ian, Henry, Rachel, Hunt, Beverley J, le Huray, Bridget, Mehenny, Susan, Miflin, Gail, Green, Jane, Stredder, Mike, Watkins, Nicholas A, McDermott, Alan, Ronaldson, Clive, Thomson, Claire, Tolkien, Zoe, Williamson, Lorna, Allan, David, Sambrook, Jennifer, Hammerton, Tracey, Bruce, David, Choudry, Fizzah, Ghvaert, Cedric, Jonston, Kirstie, Kelly, Anne, King, Andrew, Mo, Alfred, Page, Lizanne, Richardson, Penny, Senior, Peter, Umrania, Yagnesh, Wong, Henna, Burchell, Brendan, Gallacher, John, Murphy, Gavin, Newland, Adrian C, Wheatley, Keith, Greaves, Michael, Turner, Marc, Aziz, Tahir, Brain, Richard, Davies, Christine, Turner, Ruth, Wakeman, Paula, Dent, Alison, Wakeman, Alan, Anthony, Ben, Bland, Desmond, Parrondo, Willem H, Vincent, Helen, Weatherill, Candy, Forsyth, Andrea, Butterfield, Carol, Wright, Tracey, Ellis, Karen, Johnston, Kristie, Poynton, Pat, Brooks, Carolyn, Martin, Emma, Littler, Lara, Williamson, Lindsay, Blair, Donna, Ackerley, Karen, Woods, Lynn, Stanley, Sophie, Walsh, Gemma, Franklin, Gayle, Howath, Cheryl, Sharpe, Sarah, Smith, Deborah, Botham, Lauren, Williams, Caroline, Alexander, Claire, Sowerbutts, Gareth, Furnival, Diane, Thake, Michael, Patel, Shilpa, Roost, Carolyn, Sowerby, Sandra, Appleton, Mary Joy, Bays, Eileen, Bowyer, Geoff, Clarkson, Steven, Halson, Stuart, Holmes, Kate, Humphreys, Gareth, Parvin-Cooper, Lee, Towler, Jason, Addy, Joanne, Barrass, Patrica, Stennett, Louise, Burton, Susan, Dingwell, Hannah, Clarke, Victoria, Potton, Maria, Bolton, Thomas, Daynes, Michael, Spackman, Sarah, Walker, Michael, Momodu, Abudu, Fenton, James, King, Adam, Muhammad, Omer, Oates, Nicholas, Peakman, Tim, Ryan, Christine, Spreckley, Kristian, Stubbins, Craig, Williams, Joanna, Brannan, James, Mochon, Cedric, Taylor, Samantha, Warren, Kimberly, and Mant, Jonathan

Published
2019
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45. World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions

Author

Kaptoge, Stephen, Pennells, Lisa, De Bacquer, Dirk, Cooney, Marie Therese, Kavousi, Maryam, Stevens, Gretchen, Riley, Leanne Margaret, Savin, Stefan, Khan, Taskeen, Altay, Servet, Amouyel, Philippe, Assmann, Gerd, Bell, Steven, Ben-Shlomo, Yoav, Berkman, Lisa, Beulens, Joline W, Björkelund, Cecilia, Blaha, Michael, Blazer, Dan G, Bolton, Thomas, Bonita Beaglehole, Ruth, Brenner, Hermann, Brunner, Eric J, Casiglia, Edoardo, Chamnan, Parinya, Choi, Yeun-Hyang, Chowdry, Rajiv, Coady, Sean, Crespo, Carlos J, Cushman, Mary, Dagenais, Gilles R, D'Agostino Sr, Ralph B, Daimon, Makoto, Davidson, Karina W, Engström, Gunnar, Ford, Ian, Gallacher, John, Gansevoort, Ron T, Gaziano, Thomas Andrew, Giampaoli, Simona, Grandits, Greg, Grimsgaard, Sameline, Grobbee, Diederick E, Gudnason, Vilmundur, Guo, Qi, Tolonen, Hanna, Humphries, Steve, Iso, Hiroyasu, Jukema, J Wouter, Kauhanen, Jussi, Kengne, Andre Pascal, Khalili, Davood, Koenig, Wolfgang, Kromhout, Daan, Krumholz, Harlan, Lam, TH, Laughlin, Gail, Marín Ibañez, Alejandro, Meade, Tom W, Moons, Karel G M, Nietert, Paul J, Ninomiya, Toshiharu, Nordestgaard, Børge G, O'Donnell, Christopher, Palmieri, Luigi, Patel, Anushka, Perel, Pablo, Price, Jackie F, Providencia, Rui, Ridker, Paul M, Rodriguez, Beatriz, Rosengren, Annika, Roussel, Ronan, Sakurai, Masaru, Salomaa, Veikko, Sato, Shinichi, Schöttker, Ben, Shara, Nawar, Shaw, Jonathan E, Shin, Hee-Choon, Simons, Leon A, Sofianopoulou, Eleni, Sundström, Johan, Völzke, Henry, Wallace, Robert B, Wareham, Nicholas J, Willeit, Peter, Wood, David, Wood, Angela, Zhao, Dong, Woodward, Mark, Danaei, Goodarz, Roth, Gregory, Mendis, Shanthi, Onuma, Oyere, Varghese, Cherian, Ezzati, Majid, Graham, Ian, Jackson, Rod, Danesh, John, and Di Angelantonio, Emanuele

Published
2019
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46. Tackling gaps in developing life-changing treatments for dementia

Author

Mauricio, Rui, Benn, Caroline, Davis, John, Dawson, Gerry, Dawson, Lee A., Evans, Alison, Fox, Nick, Gallacher, John, Hutton, Mike, Isaac, John, Jones, Declan N.C., Jones, Lesley, Lalli, Giovanna, Libri, Vincenzo, Lovestone, Simon, Moody, Catherine, Noble, Wendy, Perry, Hugh, Pickett, James, Reynolds, David, Ritchie, Craig, Rohrer, Jonathan D., Routledge, Carol, Rowe, James, Snyder, Heather, Spires-Jones, Tara, Swartz, Jina, Truyen, Luc, and Whiting, Paul

Published
2019
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47. Real-world evidence in Alzheimer's disease: The ROADMAP Data Cube

Author

Janssen, Olin, Vos, Stephanie J.B., García-Negredo, Glòria, Tochel, Claire, Gustavsson, Anders, Smith, Michael, Ly, Amanda, Nelson, Mia, Baldwin, Helen, Sudlow, Catherine, Bexelius, Christin, Jindra, Christoph, Vaci, Nemanja, Bauermeister, Sarah, Gallacher, John, Ponjoan, Anna, Dufoil, Carole, Garre Olmo, Josep, Pedersen, Lars, Skoog, Ingmar, Hottgenroth, Antje, Jelle Visser, Pieter, van der Lei, Johan, and Diaz, Carlos

Published
2019
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