Dexter Canoy

I am a Clinical Epidemiologist at the Deep Medicine programme, University of Oxford, with research interests in using large-scale clinical and other biomedical data to improve our understanding of the determinants of population health, cardiovascular and metabolic disease aetiology, and clinical factors affecting health outcomes. I am also interested in understanding the complex nature of the relationships between body composition and health, such as in the context of obesity, ageing, frailty, and multimorbidity. I am involved in the Oxford Martin School Deep Medicine Research Programme which aims to combine expertise in medicine, epidemiology and machine learning in using Big Data in healthcare to extend our knowledge and understanding of complex disease patterns and risks, and provide novel insights into managing patients with complex conditions. I am also engaged in evaluating less commonly investigated outcomes associated with blood pressure lowering treatments using evidence from large-scale linked primary care health records (UK Clinical Practice Research Database [CPRD]) and population-based cohort studies (UK Biobank), and data from clinical trials (Blood Pressure Lowering Treatment Trialists Collaboration) to help identify ways to maximize health gains and minimize adverse effects of blood pressure lowering treatments. I have a background in Psychology (Ateneo de Manila University, Philippines), and Clinical Medicine (University of the Philippines – Philippine General Hospital), and obtained my MPhil and PHD degrees in Epidemiology at University of Cambridge. I have had the opportunity to pursue research in large-scale population-based cohorts (EPIC-Norfolk, Northern Finland Birth Cohort Studies 1966 & 1985-1986, and the Million Women Study) on body fat distribution and cardiovascular disease risk; early life factors of adult cardiovascular, metabolic and respiratory health; and determinants of women's vascular health.
My research interests include:
  • Cardiovascular disease / non-communicable disease / chronic disease epidemiology
  • Obesity, fat distribution and impact on health
  • Pharmacoepidemiology
  • Healthy ageing, frailty and multimorbidity
  • Life course epidemiology; Early life risk factors of adult health
  • Women's health; Reproductive factors, hypertensive disorders of pregnancy, and long-term disease risk
  • Big data and research methods in healthcare


  • Canoy D, Cairns BJ, Balkwill A, Wright FL, Khalil A, Beral V, Green J, Reeves G, Million Women Study Collaborators. Hypertension in pregnancy and risk of coronary heart disease and stroke: A prospective study in a large UK cohort, Int J Cardiol. 2016;222:1012-8
  • France M, Kwok S, Soran H, Williams S, Ho JH, Adam S, Canoy D, Liu Y, Durrington PN, Liver fat measured by MR spectroscopy: Estimate of imprecision and relationship with serum glycerol, caeruloplasmin and non-esterified fatty acids, Int J Mol Sci. 2016;17(7)
  • Canoy D, Yang TO, Obesity in children: bariatric surgery, BMJ Clin Evid. 2015;2015. pii: 0325
  • Canoy D, Beral V, Balkwill A, Wright FL, Kroll ME, Reeves GK, Green J, Cairns BJ, Million Women Study Collaborators. Age at menarche and risks of coronary heart and other vascular diseases in a large UK cohort, Circulation. 2015;131(3):237-44
  • Canoy D, Cholesterol-lowering statin therapy to prevent atherosclerotic cardiovascular disease - is the new guideline based on best evidence?, Prev Med. 2014;69:317-8
  • Canoy D, Barber TM, Pouta A, Hartikainen AL, McCarthy MI, Franks S, Järvelin MR, Tapanainen JS, Ruokonen A, Huhtaniemi IT, Martikainen H, Serum sex hormone-binding globulin and testosterone in relation to cardiovascular disease risk factors in young men: a population-based study, Eur J Endocrinol. 2014;170(6):863-72
  • Canoy D, Cairns BJ, Balkwill A, Wright FL, Green J, Reeves G, Beral V, Million Women Study Collaborators. Coronary heart disease incidence in women by waist circumference within categories of body mass index, Eur J Prev Cardiol. 2013;20(5):759-62
  • Canoy D, Cairns BJ, Balkwill A, Wright FL, Green J, Reeves G, Beral V, Million Women Study Collaborators. Body mass index and incident coronary heart disease in women: a population-based prospective study, BMC Med. 2013;11:87
  • Kwok S, Canoy D, Soran H, Ashton DW, Lowe GD, Wood D, Humphries SE, Durrington PN, Body fat distribution in relation to smoking and exogenous hormones in British women, Clin Endocrinol (Oxf). 2012;77(6):828-33
  • Canoy D, Bundred P, Obesity in children, BMJ Clin Evid, 2011;2011, pii:0325
  • Canoy D, Coronary, Heart disease and body fat distribution, Curr Atheroscler Rep. 2010;12(2):125-33
  • Canoy D, Pouta A, Ruokonen A, Hartikainen AL, Saikku P, Järvelin MR, Weight at birth and infancy in relation to adult leukocyte count: a population-based study of 5619 men and women followed from the fetal period to adulthood, J ClinEndocrinol Metab. 2009;94(6):1916-22
  • Canoy D, Distribution of body fat and risk of coronary heart disease in men and women, Curr Opin Cardiol. 2008;23(6):591-8
  • Canoy D, Boekholdt SM, Wareham N, Luben R, Welch A, Bingham S, Buchan I, Day N, Khaw KT, Body fat distribution and risk of coronary heart disease in men and women in the European Prospective Investigation Into Cancer and Nutrition in Norfolk cohort: a population-based prospective study Circulation. 2007;116(25):2933-43
  • Canoy D, Buchan I, Challenges in obesity epidemiology. Obes Rev. 2007;8 Suppl 1:1-11
  • Canoy D, Pekkanen J, Elliott P, Pouta A, Laitinen J, Hartikainen AL, Zitting P, Patel S, Little MP, Järvelin MR, Early growth and adult respiratory function in men and women followed from the fetal period to adulthood, Thorax. 2007;62(5):396-402


Oxford Martin School Deep Medicine Research Programme

  • This project aims to address some of the methodological challenges, as well as create opportunities, in using ‘Big Data’ in research to answer important clinical questions and inform policies aimed at improving healthcare. I am part of a team with combined expertise in medicine, epidemiology, and machine learning, to develop scalable methods for analysis of large and complex biomedical datasets. Through an interdisciplinary approach, we plan to use established analytical tools in biostatistics and epidemiology as well as and novel techniques in data mining, machine learning and deep learning to analyse large-scale and complex datasets such as primary care electronic health records (UK CPRD) and population-based cohort studies (UK Biobank).

Blood Pressure Lowering Treatment Trialists Collaboration

  • The Collaboration, which began in 1995, includes over 100 researchers leading over 50 randomised clinical trials and involving nearly 300,000 study participants. I am part of the team involved in the new phase of data collection and analyses, which will be focusing on stratified efficacy and safety of blood pressure lowering treatment. In this project, we plan to investigate the effects of blood pressure lowering on less studied vascular diseases, non-vascular disease outcomes, as well as other potential adverse health events.

Fat distribution and health

  • I am interested in understanding the importance of body composition, particularly adiposity and its distribution, in health. Using detailed imaging measures of body composition, opportunities to examine in the detail the nature of the association, magnitude of the disease risk and potential underlying mechanisms using large-scale data such as from the UK Biobank and the Oxford Biobank. For these ongoing projects, I am collaborating with Professor Jimmy Bell and his team at the University of Westminster, as well as with Professor Fredrik Karpe and his group at the University of Oxford.

Women's health

  • As part of my work to investigate vascular health determinants in women, I am continuing pursue on reproductive factors associated with long-term vascular disease risk. Further, I also examining how patterns of childhood growth relate to women’s vascular disease risk later in life. In collaboration with Professor Dame Valerie Beral and her team at the Cancer Epidemiology Unit, we will be using data from the Million Women study to investigate these research questions.

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