The CEU is highly productive, publishing over 100 papers on average every year, including influential outputs with 100s or 1000s of citations and exceptional Almetrics.
Laying foundations for new interventions, we have helped identify many genetic risk factors for CVDs and related traits, including coronary disease (Howson, Nat Genet 2017; Aragam Nat Genet in press), blood pressure (Surendran, Nat Genet 2020), and blood cell traits (Vuckovic, Cell 2020). We have pioneered studies of the plasma proteome (Sun, Nature 2018; Zheng, Nat Genet 2020), rare loss of function mutations in consanguineous populations (Saleheen, Nature 2017), and polygenic risk scores (Inouye, Nat Med in press; Lambert, Nat Genet 2021; Wand, Nature 2021).
Clinical and population health impact
Providing an actionable resource for clinical practice worldwide, we have led the development and evaluation of a new WHO CVD risk prediction approach (Kaptoge, Lancet Global Health 2019), and of the SCORE2 CVD risk prediction model for European regions (Hageman, Eur Heart J 2021). Our research has shaped the design of a lipoprotein(a)-lowering phase 3 trial (Burgess, JAMA Cardiol 2018). We have produced a high quality evidence-base for CVD prevention guidelines, for example by showing that safe limits for alcohol consumption are lower than those recommended in most current guidelines (Wood, Lancet 2018), and that depressive symptoms below the threshold for a clinical disorder are associated with higher CVD risk (Harshfield, JAMA 2020). Our work continues to underpin the UK national abdominal aortic aneurysm screening programme (Sweeting, Lancet 2018).
Creating studies, tools, and methods
We have recruited ~200,000 people in the UK and South Asia into multi-modal studies. We co-lead global consortia, including EPIC-CVD, the world’s large case-cohort study of incident CVDs. Delivering on our values of open science, we make our methods, results, and publications widely accessible, already used by 100,000s of users in over 100 countries, including: PhenoScanner (http://www.phenoscanner.medschl.cam.ac.uk/); Polygenic Score Catalog (https://www.pgscatalog.org/); OmicsPred (https://www.omicspred.org). We provide open-access tools to support molecular epidemiology, meta-analysis, and risk prediction (https://www.phpc.cam.ac.uk/ceu/erfc/programs/). Our data have enabled multiple groups to produce high-impact “secondary use” publications (eg, van der Klaauw, Cell 2019).