Tim Spector

From MEpedia, a crowd-sourced encyclopedia of ME and CFS science and history

Professor Timothy David Spector, better known as Tim D. Spector, is Head of Department, Department of Twin Research and Genetic Epidemiology at King's College London, UK.[1] Professor Spector is co-founder of ZOE, a personal nutritional technology company, and author of a number of popular science books.[2]

Notable studies[edit | edit source]

COVID Symptom Study[edit | edit source]

  • 2020, Risk of COVID-19 among front-line health-care workers and the general community: a prospective cohort study[3] - (Full text) - published as part of the COronavirus Pandemic Epidemiology (COPE) Consortium
  • 2020, Attributes and predictors of Long-COVID: analysis of COVID cases and their symptoms collected by the Covid Symptoms Study App[4] - (Full text)

Others studies[edit | edit source]

  • 2019, A metabolic profile of all-cause mortality risk identified in an observational study of 44,168 individuals - (Full text)

Books[edit | edit source]

  • Spoon-Fed: Why almost everything we’ve been told about food is wrong (2020)
  • The Diet Myth: The Real Science Behind What We Eat (2016)
  • Identically Different: Why You Can Change Your Genes (2013)

Talks, interviews and videos[edit | edit source]

Online presence[edit | edit source]

See also[edit | edit source]

Learn more[edit | edit source]

References[edit | edit source]

  1. King's College London. "Professor Tim Spector". kcl.ac.uk. Retrieved October 14, 2020.
  2. Vella, Heidi (September 22, 2020). "Citizen science: the way to beat COVID and change medicine?". Raconteur. Retrieved October 14, 2020.
  3. Nguyen, Long H.; Drew, David A.; Graham, Mark S.; Joshi, Amit D.; Guo, Chuan-Guo; Ma, Wenjie; Mehta, Raaj S.; Warner, Erica T.; Sikavi, Daniel R. (September 1, 2020). "Risk of COVID-19 among front-line health-care workers and the general community: a prospective cohort study". The Lancet Public Health. 5 (9): e475–e483. doi:10.1016/S2468-2667(20)30164-X. ISSN 2468-2667. PMC 7491202. PMID 32745512.
  4. Sudre, Carole H.; Murray, Benjamin; Varsavsky, Thomas; Graham, Mark S.; Penfold, Rose S.; Bowyer, Ruth C.; Pujol, Joan Capdevila; Klaser, Kerstin; Antonelli, Michela (October 19, 2020). "Attributes and predictors of Long-COVID: analysis of COVID cases and their symptoms collected by the Covid Symptoms Study App". medRxiv: 2020.10.19.20214494. doi:10.1101/2020.10.19.20214494.