Your browser has javascript turned off or blocked. This will lead to some parts of our website to not work properly or at all. Turn on javascript for best performance.

The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here:

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Paul Franks

Paul Franks

Principal investigator

Paul Franks

Human postprandial responses to food and potential for precision nutrition


  • Sarah E. Berry
  • Ana M. Valdes
  • David A. Drew
  • Francesco Asnicar
  • Mohsen Mazidi
  • Jonathan Wolf
  • Joan Capdevila
  • George Hadjigeorgiou
  • Richard Davies
  • Haya Al Khatib
  • Christopher Bonnett
  • Sajaysurya Ganesh
  • Elco Bakker
  • Deborah Hart
  • Massimo Mangino
  • Jordi Merino
  • Inbar Linenberg
  • Patrick Wyatt
  • Jose M. Ordovas
  • Christopher D. Gardner
  • Linda M. Delahanty
  • Andrew T. Chan
  • Nicola Segata
  • Paul W. Franks
  • Tim D. Spector

Summary, in English

Metabolic responses to food influence risk of cardiometabolic disease, but large-scale high-resolution studies are lacking. We recruited n = 1,002 twins and unrelated healthy adults in the United Kingdom to the PREDICT 1 study and assessed postprandial metabolic responses in a clinical setting and at home. We observed large inter-individual variability (as measured by the population coefficient of variation (s.d./mean, %)) in postprandial responses of blood triglyceride (103%), glucose (68%) and insulin (59%) following identical meals. Person-specific factors, such as gut microbiome, had a greater influence (7.1% of variance) than did meal macronutrients (3.6%) for postprandial lipemia, but not for postprandial glycemia (6.0% and 15.4%, respectively); genetic variants had a modest impact on predictions (9.5% for glucose, 0.8% for triglyceride, 0.2% for C-peptide). Findings were independently validated in a US cohort (n = 100 people). We developed a machine-learning model that predicted both triglyceride (r = 0.47) and glycemic (r = 0.77) responses to food intake. These findings may be informative for developing personalized diet strategies. The registration identifier is NCT03479866.


  • Genetic and Molecular Epidemiology
  • EpiHealth: Epidemiology for Health
  • EXODIAB: Excellence in Diabetes Research in Sweden

Publishing year







Nature Medicine





Document type

Journal article


Nature Publishing Group


  • Nutrition and Dietetics



Research group

  • Genetic and Molecular Epidemiology


  • ISSN: 1078-8956