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1. Genetically Predicted Type 2 Diabetes Mellitus Liability, Glycated Hemoglobin and Cardiovascular Diseases: A Wide-Angled Mendelian Randomization Study

Author

Bowen Liu, Amy M. Mason, Luanluan Sun, Emanuele Di Angelantonio, Dipender Gill, and Stephen Burgess

Subjects
mendelian randomization, type 2 diabetes mellitus, hemoglobin A1c, average blood glucose, cardiovascular diseases, Genetics, QH426-470
Abstract

(1) Aim: To investigate the causal effects of T2DM liability and glycated haemoglobin (HbA1c) levels on various cardiovascular disease outcomes, both in the general population and in non-diabetic individuals specifically. (2) Methods: We selected 243 variants as genetic instruments for T2DM liability and 536 variants for HbA1c. Linear Mendelian randomization analyses were performed to estimate the associations of genetically-predicted T2DM liability and HbA1c with 12 cardiovascular disease outcomes in 367,703 unrelated UK Biobank participants of European ancestries. We performed secondary analyses in participants without diabetes (HbA1c < 6.5% with no diagnosed diabetes), and in participants without diabetes or pre-diabetes (HbA1c < 5.7% with no diagnosed diabetes). (3) Results: Genetically-predicted T2DM liability was positively associated (p < 0.004, 0.05/12) with peripheral vascular disease, aortic valve stenosis, coronary artery disease, heart failure, ischaemic stroke, and any stroke. Genetically-predicted HbA1c was positively associated with coronary artery disease and any stroke. Mendelian randomization estimates generally shifted towards the null when excluding diabetic and pre-diabetic participants from analyses. (4) Conclusions: This genetic evidence supports causal effects of T2DM liability and HbA1c on a range of cardiovascular diseases, suggesting that improving glycaemic control could reduce cardiovascular risk in a general population, with greatest benefit in individuals with diabetes.

Published
2021
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2. Including measures of chronic kidney disease to improve cardiovascular risk prediction by SCORE2 and SCORE2-OP

Author

Kunihiro Matsushita, Stephen Kaptoge, Steven H J Hageman, Yingying Sang, Shoshana H Ballew, Morgan E Grams, Aditya Surapaneni, Luanluan Sun, Johan Arnlov, Milica Bozic, Hermann Brenner, Nigel J Brunskill, Alex R Chang, Rajkumar Chinnadurai, Massimo Cirillo, Adolfo Correa, Natalie Ebert, Kai-Uwe Eckardt, Ron T Gansevoort, Orlando Gutierrez, Farzad Hadaegh, Jiang He, Shih-Jen Hwang, Tazeen H Jafar, Simerjot K Jassal, Takamasa Kayama, Csaba P Kovesdy, Gijs W Landman, Andrew S Levey, Donald M Lloyd-Jones, Rupert W Major, Katsuyuki Miura, Paul Muntner, Girish N Nadkarni, Christoph Nowak, Takayoshi Ohkubo, Michelle J Pena, Kevan R Polkinghorne, Toshimi Sairenchi, Elke Schaeffner, Markus P Schneider, Varda Shalev, Michael G Shlipak, Marit D Solbu, Nikita Stempniewicz, James Tollitt, José M Valdivielso, Joep van der Leeuw, Angela Yee-Moon Wang, Chi-Pang Wen, Mark Woodward, Kazumasa Yamagishi, Hiroshi Yatsuya, Luxia Zhang, Jannick A N Dorresteijn, Emanuele Di Angelantonio, Frank L J Visseren, Lisa Pennells, and Josef Coresh

Subjects
Epidemiology, Cardiology and Cardiovascular Medicine
Abstract

Aims The 2021 European Society of Cardiology (ESC) guideline on cardiovascular disease (CVD) prevention categorizes moderate and severe chronic kidney disease (CKD) as high and very-high CVD risk status regardless of other factors like age and does not include estimated glomerular filtration rate (eGFR) and albuminuria in its algorithms, systemic coronary risk estimation 2 (SCORE2) and systemic coronary risk estimation 2 in older persons (SCORE2-OP), to predict CVD risk. We developed and validated an ‘Add-on’ to incorporate CKD measures into these algorithms, using a validated approach. Methods In 3,054 840 participants from 34 datasets, we developed three Add-ons [eGFR only, eGFR + urinary albumin-to-creatinine ratio (ACR) (the primary Add-on), and eGFR + dipstick proteinuria] for SCORE2 and SCORE2-OP. We validated C-statistics and net reclassification improvement (NRI), accounting for competing risk of non-CVD death, in 5,997 719 participants from 34 different datasets. Results In the target population of SCORE2 and SCORE2-OP without diabetes, the CKD Add-on (eGFR only) and CKD Add-on (eGFR + ACR) improved C-statistic by 0.006 (95%CI 0.004–0.008) and 0.016 (0.010–0.023), respectively, for SCORE2 and 0.012 (0.009–0.015) and 0.024 (0.014–0.035), respectively, for SCORE2-OP. Similar results were seen when we included individuals with diabetes and tested the CKD Add-on (eGFR + dipstick). In 57 485 European participants with CKD, SCORE2 or SCORE2-OP with a CKD Add-on showed a significant NRI [e.g. 0.100 (0.062–0.138) for SCORE2] compared to the qualitative approach in the ESC guideline. Conclusion Our Add-ons with CKD measures improved CVD risk prediction beyond SCORE2 and SCORE2-OP. This approach will help clinicians and patients with CKD refine risk prediction and further personalize preventive therapies for CVD.

Published
2022

4. Mild-to-Moderate Kidney Dysfunction and Cardiovascular Disease: Observational and Mendelian Randomization Analyses

Author

Liam Gaziano, Luanluan Sun, Matthew Arnold, Steven Bell, Kelly Cho, Stephen K. Kaptoge, Rebecca J. Song, Stephen Burgess, Daniel C. Posner, Katja Mosconi, Cassianne Robinson-Cohen, Amy M. Mason, Thomas R. Bolton, Ran Tao, Elias Allara, Petra Schubert, Lingyan Chen, James R. Staley, Natalie Staplin, Servet Altay, Pilar Amiano, Volker Arndt, Johan Ärnlöv, Elizabeth L.M. Barr, Cecilia Björkelund, Jolanda M.A. Boer, Hermann Brenner, Edoardo Casiglia, Paolo Chiodini, Jackie A. Cooper, Josef Coresh, Mary Cushman, Rachel Dankner, Karina W. Davidson, Renate T. de Jongh, Chiara Donfrancesco, Gunnar Engström, Heinz Freisling, Agustín Gómez de la Cámara, Vilmundur Gudnason, Graeme J. Hankey, Per-Olof Hansson, Alicia K. Heath, Ewout J. Hoorn, Hironori Imano, Simerjot K. Jassal, Rudolf Kaaks, Verena Katzke, Jussi Kauhanen, Stefan Kiechl, Wolfgang Koenig, Richard A. Kronmal, Cecilie Kyrø, Deborah A. Lawlor, Börje Ljungberg, Conor MacDonald, Giovanna Masala, Christa Meisinger, Olle Melander, Conchi Moreno Iribas, Toshiharu Ninomiya, Dorothea Nitsch, Børge G. Nordestgaard, Charlotte Onland-Moret, Luigi Palmieri, Dafina Petrova, Jose Ramón Quirós Garcia, Annika Rosengren, Carlotta Sacerdote, Masaru Sakurai, Carmen Santiuste, Matthias B. Schulze, Sabina Sieri, Johan Sundström, Valérie Tikhonoff, Anne Tjønneland, Tammy Tong, Rosario Tumino, Ioanna Tzoulaki, Yvonne T. van der Schouw, W.M. Monique Verschuren, Henry Völzke, Robert B. Wallace, S. Goya Wannamethee, Elisabete Weiderpass, Peter Willeit, Mark Woodward, Kazumasa Yamagishi, Raul Zamora-Ros, Elvis A. Akwo, Saiju Pyarajan, David R. Gagnon, Philip S. Tsao, Sumitra Muralidhar, Todd L. Edwards, Scott M. Damrauer, Jacob Joseph, Lisa Pennells, Peter W.F. Wilson, Seamus Harrison, Thomas A. Gaziano, Michael Inouye, Colin Baigent, Juan P. Casas, Claudia Langenberg, Nick Wareham, Elio Riboli, J.Michael Gaziano, John Danesh, Adriana M. Hung, Adam S. Butterworth, Angela M. Wood, Emanuele Di Angelantonio, Anna Koettgen, Jonathan Shaw, Robert Atkins, Paul Zimmet, Peter Whincup, Johann Willeit, Christoph Leitner, Anne Tybjaerg-Hansen, Peter Schnohr, Shoaib Afzal, David Lora Pablos, Cristina Martin Arriscado, Carmen Romero Ferreiro, Hannah Stocker, Ben Schöttker, Bernd Holleczek, Angela Chetrit, Lennart Welin, Kurt Svärdsudd, Lauren Lissner, Dominique Hange, Kirsten Mehlig, Dorothea Nagel, Paul E. Norman, Osvaldo Almeida, Leon Flicker, Jun Hata, Takanori Honda, Yoshihiko Furuta, Hiroyasu Iso, Akihiko Kitamura, Isao Muraki, Jukka T. Salonen, Tomi-Pekka Tuomainen, E. M. van Zutphen, N. M. van Schoor, Cinzia Lo Noce, Richard Kronmal, Georg Lappas, Peter M. Nilsson, Bo Hedblad, Jonathan Shaffer, Joseph Schwartz, Daichi Shimbo, Shinichi Sato, Mina Hayama-Terada, Simerjot Jassal, Thor Aspelund, Bolli Thorsson, Gunnar Sigurdsson, Layal Chaker, Kamran M. Ikram, Maryam Kavousi, Hugh Tunstall-Pedoe, Günay Can, Hüsniye Yüksel, Uğur Özkan, Hideaki Nakagawa, Yuko Morikawa, Masao Ishizaki, Edith Feskens, Johanna M Geleijnse, Daan Kromhout, Internal Medicine, Neurology, Epidemiology, Bell, Steven [0000-0001-6774-3149], Posner, Daniel C [0000-0002-3056-6924], Mason, Amy M [0000-0002-8019-0777], Allara, Elias [0000-0002-1634-8330], Staplin, Natalie [0000-0003-4482-4418], Arndt, Volker [0000-0001-9320-8684], Ärnlöv, Johan [0000-0002-6933-4637], Barr, Elizabeth LM [0000-0003-4284-1716], Boer, Jolanda MA [0000-0002-9714-4304], Brenner, Hermann [0000-0002-6129-1572], Casiglia, Edoardo [0000-0002-0003-3289], Chiodini, Paolo [0000-0003-0139-2264], Coresh, Josef [0000-0002-4598-0669], Cushman, Mary [0000-0002-7871-6143], Davidson, Karina W [0000-0002-9162-477X], de Jongh, Renate T [0000-0001-8414-3938], Engström, Gunnar [0000-0002-8618-9152], de la Cámara, Agustín Gómez [0000-0001-6827-6319], Gudnason, Vilmundur [0000-0001-5696-0084], Hankey, Graeme J [0000-0002-6044-7328], Hansson, Per-Olof [0000-0001-6323-0506], Heath, Alicia K [0000-0001-6517-1300], Hoorn, Ewout J [0000-0002-8738-3571], Imano, Hironori [0000-0002-6661-4254], Katzke, Verena [0000-0002-6509-6555], Kiechl, Stefan [0000-0002-9836-2514], Koenig, Wolfgang [0000-0002-2064-9603], Kronmal, Richard A [0000-0002-9897-7076], Kyrø, Cecilie [0000-0002-9083-8960], Ljungberg, Börje [0000-0002-4121-3753], MacDonald, Conor [0000-0002-4989-803X], Masala, Giovanna [0000-0002-5758-9069], Ninomiya, Toshiharu [0000-0003-1345-9032], Nordestgaard, Børge G [0000-0002-1954-7220], Onland-Moret, Charlotte [0000-0002-2360-913X], Palmieri, Luigi [0000-0002-4298-2642], Rosengren, Annika [0000-0002-5409-6605], Schulze, Matthias B [0000-0002-0830-5277], Sieri, Sabina [0000-0001-5201-172X], Sundström, Johan [0000-0003-2247-8454], Tikhonoff, Valérie [0000-0001-7846-0101], Tong, Tammy [0000-0002-0284-8959], Tzoulaki, Ioanna [0000-0002-4275-9328], van der Schouw, Yvonne T [0000-0002-4605-435X], Wannamethee, S Goya [0000-0001-9484-9977], Weiderpass, Elisabete [0000-0003-2237-0128], Willeit, Peter [0000-0002-1866-7159], Woodward, Mark [0000-0001-9800-5296], Yamagishi, Kazumasa [0000-0003-3301-5519], Zamora-Ros, Raul [0000-0002-6236-6804], Gagnon, David R [0000-0002-6367-3179], Tsao, Philip S [0000-0001-7274-9318], Edwards, Todd L [0000-0003-4318-6119], Damrauer, Scott M [0000-0001-8009-1632], Joseph, Jacob [0000-0002-7279-4896], Pennells, Lisa [0000-0002-8594-3061], Gaziano, Thomas A [0000-0002-5985-345X], Langenberg, Claudia [0000-0002-5017-7344], Wareham, Nick [0000-0003-1422-2993], Hung, Adriana M [0000-0002-3203-1608], Butterworth, Adam S [0000-0002-6915-9015], Di Angelantonio, Emanuele [0000-0001-8776-6719], Apollo - University of Cambridge Repository, Gaziano, Liam, Sun, Luanluan, Arnold, Matthew, Bell, Steven, Cho, Kelly, Kaptoge, Stephen K, Song, Rebecca J, Burgess, Stephen, Posner, Daniel C, Mosconi, Katja, Robinson-Cohen, Cassianne, Mason, Amy M, Bolton, Thomas R, Tao, Ran, Allara, Elia, Schubert, Petra, Chen, Lingyan, Staley, James R, Staplin, Natalie, Altay, Servet, Amiano, Pilar, Arndt, Volker, Ärnlöv, Johan, Barr, Elizabeth L M, Björkelund, Cecilia, Boer, Jolanda M A, Brenner, Hermann, Casiglia, Edoardo, Chiodini, Paolo, Cooper, Jackie A, Coresh, Josef, Cushman, Mary, Dankner, Rachel, Davidson, Karina W, de Jongh, Renate T, Donfrancesco, Chiara, Engström, Gunnar, Freisling, Heinz, de la Cámara, Agustín Gómez, Gudnason, Vilmundur, Hankey, Graeme J, Hansson, Per-Olof, Heath, Alicia K, Hoorn, Ewout J, Imano, Hironori, Jassal, Simerjot K, Kaaks, Rudolf, Katzke, Verena, Kauhanen, Jussi, Kiechl, Stefan, Koenig, Wolfgang, Kronmal, Richard A, Kyrø, Cecilie, Lawlor, Deborah A, Ljungberg, Börje, Macdonald, Conor, Masala, Giovanna, Meisinger, Christa, Melander, Olle, Moreno Iribas, Conchi, Ninomiya, Toshiharu, Nitsch, Dorothea, Nordestgaard, Børge G, Onland-Moret, Charlotte, Palmieri, Luigi, Petrova, Dafina, Garcia, Jose Ramón Quiró, Rosengren, Annika, Sacerdote, Carlotta, Sakurai, Masaru, Santiuste, Carmen, Schulze, Matthias B, Sieri, Sabina, Sundström, Johan, Tikhonoff, Valérie, Tjønneland, Anne, Tong, Tammy, Tumino, Rosario, Tzoulaki, Ioanna, van der Schouw, Yvonne T, Monique Verschuren, W M, Völzke, Henry, Wallace, Robert B, Wannamethee, S Goya, Weiderpass, Elisabete, Willeit, Peter, Woodward, Mark, Yamagishi, Kazumasa, Zamora-Ros, Raul, Akwo, Elvis A, Pyarajan, Saiju, Gagnon, David R, Tsao, Philip S, Muralidhar, Sumitra, Edwards, Todd L, Damrauer, Scott M, Joseph, Jacob, Pennells, Lisa, Wilson, Peter W F, Harrison, Seamu, Gaziano, Thomas A, Inouye, Michael, Baigent, Colin, Casas, Juan P, Langenberg, Claudia, Wareham, Nick, Riboli, Elio, Gaziano, J Michael, Danesh, John, Hung, Adriana M, Butterworth, Adam S, Wood, Angela M, Di Angelantonio, Emanuele, Internal medicine, AMS - Ageing & Vitality, AMS - Musculoskeletal Health, Amsterdam Gastroenterology Endocrinology Metabolism, Epidemiology and Data Science, APH - Aging & Later Life, and APH - Personalized Medicine

Subjects
kidney disease, General Practice, Emerging Risk Factors Collaboration/EPIC-CVD/Million Veteran Program, Coronary Disease, coronary disease, Kidney, Malalties coronàries, 1117 Public Health and Health Services, Coronary diseases, SDG 3 - Good Health and Well-being, cardiovascular disease, Risk Factors, Physiology (medical), Diabetes Mellitus, Humans, Cardiac and Cardiovascular Systems, Prospective Studies, 1102 Cardiorespiratory Medicine and Haematology, Kardiologi, Kidney diseases, Malalties cardiovasculars, Cardiovascular Diseases, Kidney Diseases, Stroke, 1103 Clinical Sciences, Mendelian Randomization Analysis, kidney diseases, stroke, Allmänmedicin, Cardiovascular diseases, Cardiovascular System & Hematology, Malalties del ronyó, Cardiology and Cardiovascular Medicine, cardiovascular diseases
Abstract

Background: End-stage renal disease is associated with a high risk of cardiovascular events. It is unknown, however, whether mild-to-moderate kidney dysfunction is causally related to coronary heart disease (CHD) and stroke. Methods: Observational analyses were conducted using individual-level data from 4 population data sources (Emerging Risk Factors Collaboration, EPIC-CVD [European Prospective Investigation into Cancer and Nutrition–Cardiovascular Disease Study], Million Veteran Program, and UK Biobank), comprising 648 135 participants with no history of cardiovascular disease or diabetes at baseline, yielding 42 858 and 15 693 incident CHD and stroke events, respectively, during 6.8 million person-years of follow-up. Using a genetic risk score of 218 variants for estimated glomerular filtration rate (eGFR), we conducted Mendelian randomization analyses involving 413 718 participants (25 917 CHD and 8622 strokes) in EPIC-CVD, Million Veteran Program, and UK Biobank. Results: There were U-shaped observational associations of creatinine-based eGFR with CHD and stroke, with higher risk in participants with eGFR values 105 mL·min –1 ·1.73 m –2 , compared with those with eGFR between 60 and 105 mL·min –1 ·1.73 m –2 . Mendelian randomization analyses for CHD showed an association among participants with eGFR –1 ·1.73 m –2 , with a 14% (95% CI, 3%–27%) higher CHD risk per 5 mL·min –1 ·1.73 m –2 lower genetically predicted eGFR, but not for those with eGFR >105 mL·min –1 ·1.73 m –2 . Results were not materially different after adjustment for factors associated with the eGFR genetic risk score, such as lipoprotein(a), triglycerides, hemoglobin A1c, and blood pressure. Mendelian randomization results for stroke were nonsignificant but broadly similar to those for CHD. Conclusions: In people without manifest cardiovascular disease or diabetes, mild-to-moderate kidney dysfunction is causally related to risk of CHD, highlighting the potential value of preventive approaches that preserve and modulate kidney function.

Published
2022

5. Incremental value of risk factor variability for cardiovascular risk prediction in individuals with type 2 diabetes: results from UK primary care electronic health records

Author

Zhe Xu, Matthew Arnold, Luanluan Sun, David Stevens, Ryan Chung, Samantha Ip, Jessica Barrett, Stephen Kaptoge, Lisa Pennells, Emanuele Di Angelantonio, Angela M Wood, Xu, Zhe [0000-0003-1519-6707], Kaptoge, Stephen [0000-0002-1155-4872], Pennells, Lisa [0000-0002-8594-3061], Di Angelantonio, Emanuele [0000-0001-8776-6719], Wood, Angela M [0000-0002-7937-304X], and Apollo - University of Cambridge Repository

Subjects
Adult, Male, Glycated Hemoglobin, Primary Health Care, Epidemiology, variability, Cholesterol, HDL, General Medicine, Cardiovascular disease, United Kingdom, risk prediction, Diabetes Mellitus, Type 2, Risk Factors, Cardiovascular Diseases, Heart Disease Risk Factors, Humans, Electronic Health Records, Female, type 2 diabetes, repeated measurements
Abstract

Background Cardiovascular disease (CVD) risk prediction models for individuals with type 2 diabetes are important tools to guide intensification of interventions for CVD prevention. We aimed to assess the added value of incorporating risk factors variability in CVD risk prediction for people with type 2 diabetes. Methods We used electronic health records (EHRs) data from 83 910 adults with type 2 diabetes but without pre-existing CVD from the UK Clinical Practice Research Datalink for 2004–2017. Using a landmark-modelling approach, we developed and validated sex-specific Cox models, incorporating conventional predictors and trajectories plus variability of systolic blood pressure (SBP), total and high-density lipoprotein (HDL) cholesterol, and glycated haemoglobin (HbA1c). Such models were compared against simpler models using single last observed values or means. Results The standard deviations (SDs) of SBP, HDL cholesterol and HbA1c were associated with higher CVD risk (P Conclusion Incorporating variability of predictors from EHRs provides a modest improvement in CVD risk discrimination for individuals with type 2 diabetes. Given that repeat measures are readily available in EHRs especially for regularly monitored patients with diabetes, this improvement could easily be achieved.

Published
2021

6. COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records

Author

Johan H Thygesen, Christopher Tomlinson, Sam Hollings, Mehrdad A Mizani, Alex Handy, Ashley Akbari, Amitava Banerjee, Jennifer Cooper, Alvina G Lai, Kezhi Li, Bilal A Mateen, Naveed Sattar, Reecha Sofat, Ana Torralbo, Honghan Wu, Angela Wood, Jonathan A C Sterne, Christina Pagel, William N Whiteley, Cathie Sudlow, Harry Hemingway, Spiros Denaxas, Hoda Abbasizanjani, Nida Ahmed, Badar Ahmed, Abdul Qadr Akinoso-Imran, Elias Allara, Freya Allery, Emanuele Di Angelantonio, Mark Ashworth, Vandana Ayyar-Gupta, Sonya Babu-Narayan, Seb Bacon, Steve Ball, Ami Banerjee, Mark Barber, Jessica Barrett, Marion Bennie, Colin Berry, Jennifer Beveridge, Ewan Birney, Lana Bojanić, Thomas Bolton, Anna Bone, Jon Boyle, Tasanee Braithwaite, Ben Bray, Norman Briffa, David Brind, Katherine Brown, Maya Buch, Dexter Canoy, Massimo Caputo, Raymond Carragher, Alan Carson, Genevieve Cezard, Jen-Yu Amy Chang, Kate Cheema, Richard Chin, Yogini Chudasama, Emma Copland, Rebecca Crallan, Rachel Cripps, David Cromwell, Vasa Curcin, Gwenetta Curry, Caroline Dale, John Danesh, Jayati Das-Munshi, Ashkan Dashtban, Alun Davies, Joanna Davies, Gareth Davies, Neil Davies, Joshua Day, Antonella Delmestri, Rachel Denholm, John Dennis, Alastair Denniston, Salil Deo, Baljean Dhillon, Annemarie Docherty, Tim Dong, Abdel Douiri, Johnny Downs, Alexandru Dregan, Elizabeth A Ellins, Martha Elwenspoek, Fabian Falck, Florian Falter, Yat Yi Fan, Joseph Firth, Lorna Fraser, Rocco Friebel, Amir Gavrieli, Moritz Gerstung, Ruth Gilbert, Clare Gillies, Myer Glickman, Ben Goldacre, Raph Goldacre, Felix Greaves, Mark Green, Luca Grieco, Rowena Griffiths, Deepti Gurdasani, Julian Halcox, Nick Hall, Tuankasfee Hama, Anna Hansell, Pia Hardelid, Flavien Hardy, Daniel Harris, Camille Harrison, Katie Harron, Abdelaali Hassaine, Lamiece Hassan, Russell Healey, Angela Henderson, Naomi Herz, Johannes Heyl, Mira Hidajat, Irene Higginson, Rosie Hinchliffe, Julia Hippisley-Cox, Frederick Ho, Mevhibe Hocaoglu, Elsie Horne, David Hughes, Ben Humberstone, Mike Inouye, Samantha Ip, Nazrul Islam, Caroline Jackson, David Jenkins, Xiyun Jiang, Shane Johnson, Umesh Kadam, Costas Kallis, Zainab Karim, Jake Kasan, Michalis Katsoulis, Kim Kavanagh, Frank Kee, Spencer Keene, Seamus Kent, Sara Khalid, Anthony Khawaja, Kamlesh Khunti, Richard Killick, Deborah Kinnear, Rochelle Knight, Ruwanthi Kolamunnage-Dona, Evan Kontopantelis, Amanj Kurdi, Ben Lacey, Alvina Lai, Andrew Lambarth, Milad Nazarzadeh Larzjan, Deborah Lawler, Thomas Lawrence, Claire Lawson, Qiuju Li, Ken Li, Miguel Bernabeu Llinares, Paula Lorgelly, Deborah Lowe, Jane Lyons, Ronan Lyons, Pedro Machado, Mary Joan Macleod, John Macleod, Evaleen Malgapo, Mamas Mamas, Mohammad Mamouei, Sinduja Manohar, Rutendo Mapeta, Javiera Leniz Martelli, David Moreno Martos, Bilal Mateen, Aoife McCarthy, Craig Melville, Rebecca Milton, Mehrdad Mizani, Marta Pineda Moncusi, Daniel Morales, Ify Mordi, Lynn Morrice, Carole Morris, Eva Morris, Yi Mu, Tanja Mueller, Lars Murdock, Vahé Nafilyan, George Nicholson, Elena Nikiphorou, John Nolan, Tom Norris, Ruth Norris, Laura North, Teri-Louise North, Dan O'Connell, Dominic Oliver, Adejoke Oluyase, Abraham Olvera-Barrios, Efosa Omigie, Sarah Onida, Sandosh Padmanabhan, Tom Palmer, Laura Pasea, Riyaz Patel, Rupert Payne, Jill Pell, Carmen Petitjean, Arun Pherwani, Owen Pickrell, Livia Pierotti, Munir Pirmohamed, Rouven Priedon, Dani Prieto-Alhambra, Alastair Proudfoot, Terry Quinn, Jennifer Quint, Elena Raffetti, Kazem Rahimi, Shishir Rao, Cameron Razieh, Brian Roberts, Caroline Rogers, Jennifer Rossdale, Safa Salim, Nilesh Samani, Christian Schnier, Roy Schwartz, David Selby, Olena Seminog, Sharmin Shabnam, Ajay Shah, Jon Shelton, James Sheppard, Shubhra Sinha, Mirek Skrypak, Martina Slapkova, Katherine Sleeman, Craig Smith, Filip Sosenko, Matthew Sperrin, Sarah Steeg, Jonathan Sterne, Serban Stoica, Maria Sudell, Luanluan Sun, Arun Karthikeyan Suseeladevi, Michael Sweeting, Matt Sydes, Rohan Takhar, Howard Tang, Johan Thygesen, George Tilston, Claire Tochel, Clea du Toit, Renin Toms, Fatemeh Torabi, Julia Townson, Adnan Tufail, Tapiwa Tungamirai, Susheel Varma, Sebastian Vollmer, Venexia Walker, Tianxiao Wang, Huan Wang, Alasdair Warwick, Ruth Watkinson, Harry Watson, William Whiteley, Hannah Whittaker, Harry Wilde, Tim Wilkinson, Gareth Williams, Michelle Williams, Richard Williams, Eloise Withnell, Charles Wolfe, Lucy Wright, Jinge Wu, Jianhua Wu, Tom Yates, Francesco Zaccardi, Haoting Zhang, Huayu Zhang, Luisa Zuccolo, Apollo - University of Cambridge Repository, Consortium, Longitudinal Health and Wellbeing COVID-19 National Core Study and the CVD-COVID-UK/COVID-IMPACT, and Khalid, S

Subjects
SARS-CoV-2, Medicine (miscellaneous), COVID-19, Health Informatics, State Medicine, Cohort Studies, COVID-19 Testing, Health Information Management, England, Longitudinal Health and Wellbeing COVID-19 National Core Study and the CVD-COVID-UK/COVID-IMPACT Consortium, Electronic Health Records, Humans, Decision Sciences (miscellaneous), England/epidemiology, COVID-19/epidemiology
Abstract

Background Updatable estimates of COVID-19 onset, progression, and trajectories underpin pandemic mitigation efforts. To identify and characterise disease trajectories, we aimed to define and validate ten COVID-19 phenotypes from nationwide linked electronic health records (EHR) using an extensible framework. Methods In this cohort study, we used eight linked National Health Service (NHS) datasets for people in England alive on Jan 23, 2020. Data on COVID-19 testing, vaccination, primary and secondary care records, and death registrations were collected until Nov 30, 2021. We defined ten COVID-19 phenotypes reflecting clinically relevant stages of disease severity and encompassing five categories: positive SARS-CoV-2 test, primary care diagnosis, hospital admission, ventilation modality (four phenotypes), and death (three phenotypes). We constructed patient trajectories illustrating transition frequency and duration between phenotypes. Analyses were stratified by pandemic waves and vaccination status. Findings Among 57 032 174 individuals included in the cohort, 13 990 423 COVID-19 events were identified in 7 244 925 individuals, equating to an infection rate of 12·7% during the study period. Of 7 244 925 individuals, 460 737 (6·4%) were admitted to hospital and 158 020 (2·2%) died. Of 460 737 individuals who were admitted to hospital, 48 847 (10·6%) were admitted to the intensive care unit (ICU), 69 090 (15·0%) received non-invasive ventilation, and 25 928 (5·6%) received invasive ventilation. Among 384 135 patients who were admitted to hospital but did not require ventilation, mortality was higher in wave 1 (23 485 [30·4%] of 77 202 patients) than wave 2 (44 220 [23·1%] of 191 528 patients), but remained unchanged for patients admitted to the ICU. Mortality was highest among patients who received ventilatory support outside of the ICU in wave 1 (2569 [50·7%] of 5063 patients). 15 486 (9·8%) of 158 020 COVID-19-related deaths occurred within 28 days of the first COVID-19 event without a COVID-19 diagnoses on the death certificate. 10 884 (6·9%) of 158 020 deaths were identified exclusively from mortality data with no previous COVID-19 phenotype recorded. We observed longer patient trajectories in wave 2 than wave 1. Interpretation Our analyses illustrate the wide spectrum of disease trajectories as shown by differences in incidence, survival, and clinical pathways. We have provided a modular analytical framework that can be used to monitor the impact of the pandemic and generate evidence of clinical and policy relevance using multiple EHR sources. Funding British Heart Foundation Data Science Centre, led by Health Data Research UK.

Published
2021

7. Genetically Predicted Type 2 Diabetes Mellitus Liability, Glycated Hemoglobin and Cardiovascular Diseases: A Wide-Angled Mendelian Randomization Study

Author

Amy M. Mason, Dipender Gill, Bowen Liu, Luanluan Sun, Stephen Burgess, Emanuele Di Angelantonio, Gill, Dipender [0000-0001-7312-7078], and Apollo - University of Cambridge Repository

Subjects
Male, medicine.medical_specialty, endocrine system diseases, type 2 diabetes mellitus, Population, Coronary Artery Disease, QH426-470, Coronary artery disease, chemistry.chemical_compound, Internal medicine, Diabetes mellitus, Mendelian randomization, Genetics, medicine, Humans, Genetic Predisposition to Disease, education, Stroke, hemoglobin A1c, Genetics (clinical), Genetic Association Studies, average blood glucose, Ischemic Stroke, Genetics & Heredity, Glycated Hemoglobin, Heart Failure, Peripheral Vascular Diseases, 0604 Genetics, education.field_of_study, Science & Technology, business.industry, Vascular disease, Communication, Type 2 Diabetes Mellitus, nutritional and metabolic diseases, Genetic Variation, Aortic Valve Stenosis, Mendelian Randomization Analysis, Middle Aged, medicine.disease, cardiovascular diseases, chemistry, Diabetes Mellitus, Type 2, mendelian randomization, Female, Glycated hemoglobin, business, Life Sciences & Biomedicine
Abstract

(1) Aim: To investigate the causal effects of T2DM liability and glycated haemoglobin (HbA1c) levels on various cardiovascular disease outcomes, both in the general population and in non-diabetic individuals specifically. (2) Methods: We selected 243 variants as genetic instruments for T2DM liability and 536 variants for HbA1c. Linear Mendelian randomization analyses were performed to estimate the associations of genetically-predicted T2DM liability and HbA1c with 12 cardiovascular disease outcomes in 367,703 unrelated UK Biobank participants of European ancestries. We performed secondary analyses in participants without diabetes (HbA1c < 6.5% with no diagnosed diabetes), and in participants without diabetes or pre-diabetes (HbA1c < 5.7% with no diagnosed diabetes). (3) Results: Genetically-predicted T2DM liability was positively associated (p < 0.004, 0.05/12) with peripheral vascular disease, aortic valve stenosis, coronary artery disease, heart failure, ischaemic stroke, and any stroke. Genetically-predicted HbA1c was positively associated with coronary artery disease and any stroke. Mendelian randomization estimates generally shifted towards the null when excluding diabetic and pre-diabetic participants from analyses. (4) Conclusions: This genetic evidence supports causal effects of T2DM liability and HbA1c on a range of cardiovascular diseases, suggesting that improving glycaemic control could reduce cardiovascular risk in a general population, with greatest benefit in individuals with diabetes.

Published
2021

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