Associate Professor Derrick Bennett
Derrick Bennett
BSc MSc PhD CStat
Associate Professor
Derrick has a BSc (Hons) in Mathematics and Statistics, an MSc in Medical Statistics, and a PhD in Epidemiology and Statistics. He has been a Royal Statistical Society accredited Chartered Statistician since 2005.
His research is interdisciplinary, integrative and collaborative and uses large-scale observational studies and randomised trials to generate reliable evidence for the prevention of premature deaths and disability from chronic diseases. Derrick's work involves applying statistical, epidemiological, computational, and genetic tools to understand associations of exposures with chronic diseases.
His research aims to drive improvements in population health by identifying novel treatment targets and implementing precision strategies for primary and secondary prevention of major disease outcomes such as cardiovascular disease, stroke, diabetes and cancer.
Derrick co-leads the Statistical Group in the China Kadoorie Biobank and oversees a portfolio of research related to aging, cardiovascular, respiratory, and lifestyle factors. He is responsible for ensuring that the study design methodology is robust, appropriate and deliverable as well as for securing grant income as the statistical lead.
Derrick also co-leads the Principles of Data Science module of the MSc in Global Health Science and Epidemiology, and leads the curriculum development for data science teaching. He is currently supervising several MSc and DPhil students.
He has also contributed chapters to four textbooks and was named as a highly cited researcher in 2018 for papers that rank in the top 1% in his field of research. In 2022 he was listed among the top 1000 scientists in the UK in the Research.com Medicine rankings.
Recent publications
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Measured and genetically predicted protein levels and cardiovascular diseases in UK Biobank and China Kadoorie Biobank.
Journal article
Lind L. et al, (2024), Nat Cardiovasc Res
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Daily steps are a predictor of, but perhaps not a modifiable risk factor for Parkinson’s Disease: findings from the UK Biobank
Preprint
Acquah A. et al, (2024)
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Proteomic aging clock predicts mortality and risk of common age-related diseases in diverse populations.
Journal article
Argentieri MA. et al, (2024), Nat Med
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Causal relevance of different blood pressure traits on risk of cardiovascular diseases: GWAS and Mendelian randomisation in 100,000 Chinese adults
Journal article
Pozarickij A. et al, (2024), Nature Communications
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Author Correction: Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortality.
Journal article
Yuan H. et al, (2024), NPJ Digit Med, 7