UK Biobank: opportunities for cardiovascular research.
TLDR
It is demonstrated conclusively that a continuous increase in blood pressure corresponds with an increased risk of vascular death across all age groups, and the importance of a large sample size (about 500 000 participants) for detecting this association is illustrated.Abstract:
Cardiovascular diseases are a major cause of morbidity and mortality, accounting for 45% of all deaths in European countries in 20161 and almost a third of deaths worldwide in 2013.2 A similar pattern is observed in the UK where cardiovascular diseases were responsible for 27% of deaths in 2014, with coronary heart disease resulting in the largest number of deaths attributable to a single cause (n = ∼69 000) whilst stroke is the third biggest cause (n = ∼39 000).3 Although age-standardized cardiovascular disease mortality rates are decreasing worldwide, the total deaths and burden as measured through disability-adjusted life years of cardiovascular diseases are increasing.4,5 Furthermore, in the UK, cardiovascular risk factors such as high blood pressure and high cholesterol are among the leading causes of disease burden.6
Epidemiological studies have historically played an essential role in identifying the causes and consequences of cardiovascular disease and have resulted in improvements in prevention and treatment. The seminal US-based Framingham Heart Study which recruited 5200 participants between 1948 and 1952, was integral in identifying a range of important risk factors for cardiovascular disease, such as high blood pressure, a high cholesterol level, cigarette smoking, obesity and physical inactivity, and consequently shifted the focus from management to preventative strategies for cardiovascular disease.7 This, together with findings from other epidemiological studies, such as the Seven Countries Study and the MONICA project,8 have been influential in leading to treatments for the primary and secondary prevention of cardiovascular events, most notably statins (that act to lower cholesterol levels), and anithypertensives.9,10
Epidemiological studies such as the Framingham Heart Study with moderate sample sizes are useful in detecting risk factors with large effects on common outcomes; however, they lack statistical power to reliably identify risk factors which have small to moderate effects or to assess associations with disease across subgroups of the population. The need for large sample sizes has led to collaborative efforts, such as the Prospective Studies Collaboration (an individual participant meta-analysis of data from 61 studies and more than a million participants11) that has demonstrated conclusively that a continuous increase in blood pressure corresponds with an increased risk of vascular death across all age groups (see Figure Figure11 that illustrates the importance of a large sample size (about 500 000 participants) for detecting this association).12 Sample size is also of particular importance in the current era of genome-wide association studies, where many investigations are aiming to detect either small effects from common variants or large effects from rare variants.13
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Figure 1
Absolute risk of ischaemic heart disease mortality by usual systolic blood pressure and age at risk in 5000, 50 000, and 500 000 participants. Unpublished figure containing data from the Prospective Studies Collaboration, obtained through personal communication. CI, confidence interval; IHD, ischaemic heart disease.read more
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Posted ContentDOI
Genome-wide genetic data on ~500,000 UK Biobank participants
Clare Bycroft,Colin Freeman,Desislava Petkova,Lloyd T. Elliott,Kevin Sharp,Allan Motyer,Damjan Vukcevic,Olivier Delaneau,Jared O'Connell,Adrian Cortes,S Welsh,Gil McVean,Stephen Leslie,Peter Donnelly,Jonathan Marchini +14 more
TL;DR: The UK Biobank project is a large prospective cohort study of ~500,000 individuals from across the United Kingdom, aged between 40-69 at recruitment, and a set of analyses that reveal properties of the genetic data – such as population structure and relatedness – that can be important for downstream analyses are conducted.
Journal ArticleDOI
Comparison of risk factor associations in UK Biobank against representative, general population based studies with conventional response rates: prospective cohort study and individual participant meta-analysis
G. David Batty,G. David Batty,Catharine R. Gale,Catharine R. Gale,Mika Kivimäki,Ian J. Deary,Steven Bell,Steven Bell,Steven Bell +8 more
TL;DR: Risk factor levels and mortality rates were typically more favourable in UK Biobank participants relative to the HSE-SHS consortium, and close agreement was seen between studies.
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The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions
Thomas J. Littlejohns,Jo Holliday,Lorna Gibson,Lorna Gibson,Steve Garratt,Niels Oesingmann,Fidel Alfaro-Almagro,Jimmy D. Bell,Chris Boultwood,Rory Collins,Megan Conroy,Nicola Crabtree,Nicola Doherty,Alejandro F. Frangi,Alejandro F. Frangi,Nicholas C. Harvey,Paul Leeson,Karla L. Miller,Stefan Neubauer,Steffen E. Petersen,Jonathan Sellors,Simon Sheard,Stephen M. Smith,Cathie Sudlow,Paul M. Matthews,Naomi E. Allen +25 more
TL;DR: An in-depth overview of the imaging enhancement of the UK Biobank, including the data collected, how it is managed and processed, and future directions is provided.
Journal ArticleDOI
Brief introduction of medical database and data mining technology in big data era
Jin Yang,Jin Yang,Yuanjie Li,Qingqing Liu,Qingqing Liu,Li Li,Aozi Feng,Tianyi Wang,Shuai Zheng,Anding Xu,Jun Lyu,Jun Lyu +11 more
TL;DR: This work has introduced several databases and data mining techniques to help a wide range of clinical researchers better understand and apply database technology.
Journal ArticleDOI
Night Shift Work, Genetic Risk, and Type 2 Diabetes in the UK Biobank.
Céline Vetter,Céline Vetter,Céline Vetter,Hassan S. Dashti,Hassan S. Dashti,Jacqueline M. Lane,Jacqueline M. Lane,Simon G. Anderson,Simon G. Anderson,Eva S. Schernhammer,Eva S. Schernhammer,Martin K. Rutter,Martin K. Rutter,Richa Saxena,Richa Saxena,Frank A.J.L. Scheer,Frank A.J.L. Scheer,Frank A.J.L. Scheer +17 more
TL;DR: It is shown that night shift work, especially rotating shift work including night shifts, is associated with higher type 2 diabetes odds and that the number of night shifts worked per month appears most relevant for type 1 diabetes odds, a novel finding that warrants replication.
References
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Journal ArticleDOI
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Santos,Maheswar Satpathy,Monika Sawhney,Mete Saylan,Peter Scarborough,Ben Schöttker,Jürgen C Schmidt,Ione Jayce Ceola Schneider,Austin E Schumacher,David C. Schwebel,James Scott,Sadaf G. Sepanlou,Edson Serván-Mori,Katya Anne Shackelford,Amira Shaheen,Saeid Shahraz,Marina Shakh-Nazarova,Siyi Shangguan,Jun She,Sara Sheikhbahaei,Donald S. Shepard,Kenji Shibuya,Yukito Shinohara,Kawkab Shishani,Ivy Shiue,Rupak Shivakoti,Mark G. Shrime,Inga Dora Sigfusdottir,Donald H. Silberberg,Andrea P. Silva,Edgar P. Simard,Shireen Sindi,Jasvinder A. Singh,Lavanya Singh,Edgar Sioson,Vegard Skirbekk,Karen Sliwa,Samuel So,Michael Soljak,Samir Soneji,Sergey Soshnikov,Luciano A. Sposato,Chandrashekhar T Sreeramareddy,Jeffrey D. Stanaway,Vasiliki Stathopoulou,Kyle Steenland,Claudia Stein,Caitlyn Steiner,Antony Stevens,Heidi Stöckl,Kurt Straif,Konstantinos Stroumpoulis,Lela Sturua,Bruno F. Sunguya,Soumya Swaminathan,Mamta Swaroop,Bryan L. Sykes,Karen M. Tabb,Ken Takahashi,Roberto Tchio Talongwa,Feng Tan,David Tanne,Marcel Tanner,Marcel Tanner,Mohammad Tavakkoli,Braden Te Ao,Carolina Maria Teixeira,Tara Templin,Eric Y. Tenkorang,Abdullah Sulieman Terkawi,Bernadette Thomas,Andrew L. Thorne-Lyman,Amanda G. Thrift,George D. Thurston,Taavi Tillmann,David L. Tirschwell,Imad M. Tleyjeh,Imad M. Tleyjeh,Marcello Tonelli,Fotis Topouzis,Jeffrey A. Towbin,Hideaki Toyoshima,Jefferson Traebert,Bach Xuan Tran,Thomas Truelsen,Ulises Trujillo,Matias Trillini,Zacharie Tsala Dimbuene,Miltiadis K. Tsilimbaris,E. Murat Tuzcu,Clotilde Ubeda,Uche S. Uchendu,Kingsley N. Ukwaja,Eduardo A. Undurraga,Andrew Vallely,Steven van de Vijver,Coen H. Van Gool,Yuri Y Varakin,Tommi Vasankari,Ana Maria Nogales Vasconcelos,Monica S. Vavilala,Narayanaswamy Venketasubramanian,Lakshmi Vijayakumar,Salvador Villalpando,Francesco Saverio Violante,Vasiliy Victorovich Vlassov,Gregory R. Wagner,Stephen G. Waller,JianLi Wang,Linhong Wang,Xiao Rong Wang,Yanping Wang,Tati S. Warouw,Scott Weichenthal,Elisabete Weiderpass,Robert G. Weintraub,Robert G. Weintraub,Wang Wenzhi,Andrea Werdecker,K. Ryan Wessells,Ronny Westerman,Harvey Whiteford,James D. Wilkinson,Thomas N. Williams,Solomon Meseret Woldeyohannes,Charles D.A. Wolfe,Timothy M. Wolock,Anthony D. Woolf,John Q. Wong,Jonathan L. Wright,Sarah Wulf,Brittany Wurtz,Gelin Xu,Yang Yang,Yuichiro Yano,Hiroshi Yatsuya,Paul S. F. Yip,Naohiro Yonemoto,Seok Jun Yoon,Mustafa Z. Younis,Chuanhua Yu,Kim Yun Jin,Maysaa El Sayed Zaki,Mohammed Fouad Zamakhshary,Hajo Zeeb,Yong Zhang,Yong Zhao,Yingfeng Zheng,Jun Zhu,Shankuan Zhu,David Zonies,Xiao Nong Zou,Joseph R. Zunt,Theo Vos,Alan D. Lopez,Alan D. Lopez,Christopher J L Murray,Gabriel Alcalá-Cerra,Howard Hu,Nadim E. Karam,Nsanzimana Sabin,A. M. Temesgen +731 more
TL;DR: In the Global Burden of Disease Study 2013 (GBD 2013) as discussed by the authors, the authors used the GBD 2010 methods with some refinements to improve accuracy applied to an updated database of vital registration, survey, and census data.
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