Joint Principal Investigators
Professor John Danesh (University of Cambridge), Dr Emanuele Di Angelantonio (University of Cambridge), Angela Wood (University of Cambridge), Stephen Kaptoge (University of Cambridge), Lisa Pennells (University of Cambridge)
Our goal is to improve the prevention of CVDs in the UK and globally by developing and evaluating risk management tools, strategies, and interventions. To date, our key approaches have included meta-analysis of individual participant data from multiple prospective studies, evaluating the incremental predictive value of emerging CVD risk markers, use of polygenic risk scores, public health modelling, randomised trials and other methods capable of yielding rapidly actionable insights. Our findings have been cited in multiple CVD guidelines (eg, WHO, ESC, European Atheroslerosis Society) and continue to underpin the UK national AAA screening programme.
For example, the CEU has led the development and evaluation of the WHO CVD risk prediction model, calibrated to 21 global regions (Kaptoge, Lancet Global Health 2019), now adopted into the WHO package of essential noncommunicable disease interventions worldwide. We also led development and evaluation of CVD risk prediction algorithms for European regions (“SCORE2”), adopted by the 2021 ESC Guidelines on CVD Prevention in Clinical Practice (Hageman, Eur Heart J 2021) with translation and research applications facilitated by the ESC software app and statistical software provided by CEU.
The CEU has led methodological work showing nearly equal performance of four different risk scores after simple re-calibration (Pennells, Eur Heart J 2019), suggesting the need to focus on widespread use of any recalibrated algorithm. We have also led an RCT of 1000 participants suggesting provision of CVD risk information (whether phenotypic or genotypic) does not affect health-related behaviours, risk factors or well-being (Silarova, Heart 2019).
Our current research initiatives are focused on enhancing equity and efficiency in CVD risk assessment and screening. We are applying novel risk prediction and modelling approaches (including biostatistical, machine learning and AI tools) to diverse population-wide populations, to develop, evaluate and translate the use of equitable genomic tools, screening prioritisation tools and multi-factorial risk scores for clinical practice.