Human postprandial responses to food and potential for precision nutrition

Sarah Berry; Ana 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 Ordovas; Christopher Gardner; Linda Delahanty; Andrew Chan; Nicola Segata; Paul Franks; Tim Spector

doi:10.1038/s41591-020-0934-0

Human postprandial responses to food and potential for precision nutrition

Sarah Berry, Ana 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 Ordovas, Christopher GardnerLinda Delahanty, Andrew Chan, Nicola Segata, Paul Franks, Tim Spector

King's College London

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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%, 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.

Original language	English
Pages (from-to)	964-973
Number of pages	10
Journal	Nature Medicine
Volume	26
Issue number	6
Early online date	11 Jun 2020
DOIs	https://doi.org/10.1038/s41591-020-0934-0
Publication status	Published - 11 Jun 2020

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Human postprandial responses to food and potential_BERRY_Published11June2020_GREEN AAMAccepted author manuscript, 3.52 MB

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Berry, S., Valdes, A., Drew, D. A., Asnicar, F., Mazidi, M., Wolf, J., Capdevila, J., Hadjigeorgiou, G., Davies, R., Al Khatib, H., Bonnett, C., Ganesh, S., Bakker, E., Hart, D., Mangino, M., Merino, J., Linenberg, I., Wyatt, P., Ordovas, J., ... Spector, T. (2020). Human postprandial responses to food and potential for precision nutrition. Nature Medicine, 26(6), 964-973. https://doi.org/10.1038/s41591-020-0934-0

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title = "Human postprandial responses to food and potential for precision nutrition",

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%, 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.",

author = "Sarah Berry and Ana Valdes and Drew, {David A} and Francesco Asnicar and Mohsen Mazidi and Jonathan Wolf and Joan Capdevila and George Hadjigeorgiou and Richard Davies and {Al Khatib}, Haya and Christopher Bonnett and Sajaysurya Ganesh and Elco Bakker and Deborah Hart and Massimo Mangino and Jordi Merino and Inbar Linenberg and Patrick Wyatt and Jose Ordovas and Christopher Gardner and Linda Delahanty and Andrew Chan and Nicola Segata and Paul Franks and Tim Spector",

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doi = "10.1038/s41591-020-0934-0",

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Berry, S , Valdes, A, Drew, DA, Asnicar, F, Mazidi, M, Wolf, J, Capdevila, J, Hadjigeorgiou, G, Davies, R, Al Khatib, H, Bonnett, C, Ganesh, S, Bakker, E, Hart, D, Mangino, M, Merino, J, Linenberg, I, Wyatt, P, Ordovas, J, Gardner, C, Delahanty, L, Chan, A, Segata, N, Franks, P & Spector, T 2020, 'Human postprandial responses to food and potential for precision nutrition', Nature Medicine, vol. 26, no. 6, pp. 964-973. https://doi.org/10.1038/s41591-020-0934-0

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T1 - Human postprandial responses to food and potential for precision nutrition

AU - Berry, Sarah

AU - Valdes, Ana

AU - Drew, David A

AU - Asnicar, Francesco

AU - Mazidi, Mohsen

AU - Wolf, Jonathan

AU - Capdevila, Joan

AU - Hadjigeorgiou, George

AU - Davies, Richard

AU - Al Khatib, Haya

AU - Bonnett, Christopher

AU - Ganesh, Sajaysurya

AU - Bakker, Elco

AU - Hart, Deborah

AU - Mangino, Massimo

AU - Merino, Jordi

AU - Linenberg, Inbar

AU - Wyatt, Patrick

AU - Ordovas, Jose

AU - Gardner, Christopher

AU - Delahanty, Linda

AU - Chan, Andrew

AU - Segata, Nicola

AU - Franks, Paul

AU - Spector, Tim

PY - 2020/6/11

Y1 - 2020/6/11

N2 - 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.

AB - 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.

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U2 - 10.1038/s41591-020-0934-0

DO - 10.1038/s41591-020-0934-0

M3 - Article

SN - 1078-8956

VL - 26

SP - 964

EP - 973

JO - Nature Medicine

JF - Nature Medicine

IS - 6

ER -

Human postprandial responses to food and potential for precision nutrition

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