Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

Human postprandial responses to food and potential for precision nutrition

Berry, Sarah E. ; Valdes, Ana M. ; Drew, David A. ; Asnicar, Francesco ; Mazidi, Mohsen ; Wolf, Jonathan ; Capdevila, Joan ; Hadjigeorgiou, George ; Davies, Richard and Al Khatib, Haya , et al. (2020) In Nature Medicine 26(6). p.964-973
Abstract

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%,... (More)

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 ClinicalTrials.gov registration identifier is NCT03479866.

(Less)
Please use this url to cite or link to this publication:
author
; ; ; ; ; ; ; ; and , et al. (More)
; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; and (Less)
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Nature Medicine
volume
26
issue
6
pages
10 pages
publisher
Nature Publishing Group
external identifiers
  • pmid:32528151
  • scopus:85086337950
ISSN
1078-8956
DOI
10.1038/s41591-020-0934-0
language
English
LU publication?
yes
id
9f93c9e9-8bf2-4c5f-8dd9-31a36c014c31
date added to LUP
2020-06-30 15:21:15
date last changed
2024-06-13 19:00:51
@article{9f93c9e9-8bf2-4c5f-8dd9-31a36c014c31,
  abstract     = {{<p>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 ClinicalTrials.gov registration identifier is NCT03479866.</p>}},
  author       = {{Berry, Sarah E. and Valdes, Ana M. and Drew, David A. and Asnicar, Francesco and Mazidi, Mohsen and Wolf, Jonathan and Capdevila, Joan and Hadjigeorgiou, George and Davies, Richard and Al Khatib, Haya and Bonnett, Christopher and Ganesh, Sajaysurya and Bakker, Elco and Hart, Deborah and Mangino, Massimo and Merino, Jordi and Linenberg, Inbar and Wyatt, Patrick and Ordovas, Jose M. and Gardner, Christopher D. and Delahanty, Linda M. and Chan, Andrew T. and Segata, Nicola and Franks, Paul W. and Spector, Tim D.}},
  issn         = {{1078-8956}},
  language     = {{eng}},
  number       = {{6}},
  pages        = {{964--973}},
  publisher    = {{Nature Publishing Group}},
  series       = {{Nature Medicine}},
  title        = {{Human postprandial responses to food and potential for precision nutrition}},
  url          = {{http://dx.doi.org/10.1038/s41591-020-0934-0}},
  doi          = {{10.1038/s41591-020-0934-0}},
  volume       = {{26}},
  year         = {{2020}},
}