Human postprandial responses to food and potential for precision nutrition
Open accessAbstract
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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Studies like PREDICT 1 have demonstrated significant variability in postprandial glycemic responses (PPGRs) among individuals, even among those consuming the same standardized meal.
“Studies like the PREDICT 1 have demonstrated significant variability in PPGRs among individuals, even among those consuming the same standardized meal (13).”
From Personalized nutrition in type 2 diabetes remission: application of digital twin technology for predictive glycemic control (2024) · cites this paper as [13]
Personalized approaches to dietary interventions are important, and one-size-fits-all dietary recommendations are challenged.
“This aligns with findings from PREDICT 1, which underscore the importance of personalized approaches to dietary interventions and challenge the efficacy of one-size-fits-all dietary recommendations (13).”
From Personalized nutrition in type 2 diabetes remission: application of digital twin technology for predictive glycemic control (2024) · cites this paper as [13]
PPGR variability is largely driven by modifiable factors, such as gut microbiome composition, meal timing, and physical activity, rather than genetic factors alone.
“Such variability is largely driven by modifiable factors, such as gut microbiome composition, meal timing, and physical activity, rather than genetic factors alone.”
From Personalized nutrition in type 2 diabetes remission: application of digital twin technology for predictive glycemic control (2024) · cites this paper as [13]
In PREDICT1, meal macronutrients influenced postprandial glycemia (15.4%) more than gut microbiome factors (6.0%).
“In contrast, meal macronutrients influenced postprandial glycemia (15.4%) more than gut microbiome factors (6.0%).”
From Beyond Calories: Individual Metabolic and Hormonal Adaptations Driving Variability in Weight Management—A State-of-the-Art Narrative Review (2024) · cites this paper as [94]
In PREDICT1, gut microbiome composition explained 7.1% of the variance in postprandial lipemia, more than meal macronutrients (3.6%).
“Person-specific factors like gut microbiome composition explained 7.1% of the variance in postprandial lipemia, more than meal macronutrients (3.6%).”
From Beyond Calories: Individual Metabolic and Hormonal Adaptations Driving Variability in Weight Management—A State-of-the-Art Narrative Review (2024) · cites this paper as [94]
In PREDICT1, machine learning models predicted triglyceride and glycemic responses and were validated in the US cohort with similar accuracy.
“The ML models developed from these data predicted triglyceride (r = 0.47, p < 0.001) and glycemic (r = 0.77, p < 0.001) responses, validated in the US cohort with similar accuracy (r = 0.42 for triglyceride, r = 0.75 for glucose).”
From Beyond Calories: Individual Metabolic and Hormonal Adaptations Driving Variability in Weight Management—A State-of-the-Art Narrative Review (2024) · cites this paper as [94]
Recent precision-based trials underscore the efficacy of personalized weight loss interventions.
“Recent precision-based trials underscore the efficacy of personalized weight loss interventions, providing promising data for future, individualized approaches.”
From Beyond Calories: Individual Metabolic and Hormonal Adaptations Driving Variability in Weight Management—A State-of-the-Art Narrative Review (2024) · cites this paper as [94]
In PREDICT1, genetic factors explained 9.5% of glycemic response variance, with less influence on triglyceride and C-peptide responses.
“Genetic factors showed a modest impact, explaining 9.5% of glycemic response variance (p < 0.05), with less influence on triglyceride (0.8%) and C-peptide responses (0.2%) [94].”
From Beyond Calories: Individual Metabolic and Hormonal Adaptations Driving Variability in Weight Management—A State-of-the-Art Narrative Review (2024) · cites this paper as [94]
PREDICT1 underscores significant variability in metabolic responses between individuals, even among twins, due to modifiable factors, supporting the potential for personalized nutrition strategies in reducing cardiometabolic risk.
“This study underscores significant variability in metabolic responses between individuals, even among twins, due to modifiable factors, which supports the potential for personalized nutrition strategies in reducing cardiometabolic risk [94].”
From Beyond Calories: Individual Metabolic and Hormonal Adaptations Driving Variability in Weight Management—A State-of-the-Art Narrative Review (2024) · cites this paper as [94]
In the PREDICT1 study, there was substantial variation in postprandial triglyceride, glucose, and insulin responses to identical meals.
“The results showed substantial variation in postprandial triglyceride (103% coefficient of variation), glucose (68%), and insulin (59%) responses to identical meals.”
From Beyond Calories: Individual Metabolic and Hormonal Adaptations Driving Variability in Weight Management—A State-of-the-Art Narrative Review (2024) · cites this paper as [94]
AI models have demonstrated potential in predicting metabolic responses such as glycemic variability to specific foods.
“AI models have demonstrated remarkable potential in predicting metabolic responses, such as glycemic variability, to specific foods.”
From Beyond Calories: Individual Metabolic and Hormonal Adaptations Driving Variability in Weight Management—A State-of-the-Art Narrative Review (2024) · cites this paper as [94]
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- Authors
- Berry, S. E., Valdes, A. M., 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. M., Gardner, C. D., Delahanty, L. M., Chan, A. T., Segata, N., Franks, P. W., & Spector, T. D.
- Affiliations
- King's College London (GB)
- University of Nottingham (GB)
- Harvard University (US)
- University of Trento (IT)
- ZOE (United Kingdom) (GB)
- Broad Institute (US)
- Stanford University (US)
- Corresponding author
- Valdes, Ana M.
- Spector, Tim D.
- ORCID
- Berry, Sarah E.
- Valdes, Ana M.
- Drew, David A.
- Asnicar, Francesco
- Mazidi, Mohsen
- Wolf, Jonathan
- Capdevila, Joan
- Hadjigeorgiou, George
- Davies, Richard
- Al Khatib, Haya
- Bonnett, Christopher
- Ganesh, Sajaysurya
- Bakker, Elco
- Hart, Deborah
- Mangino, Massimo
- Merino, Jordi
- Linenberg, Inbar
- Wyatt, Patrick
- Ordovas, Jose M.
- Gardner, Christopher D.
- Delahanty, Linda M.
- Chan, Andrew T.
- Segata, Nicola
- Franks, Paul W.
- Spector, Tim D.
- Venue
- Nature Medicine
- Published
- 2020-06-01
- Volume
- 26
- Issue
- 6
- Pages
- 964-973
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- PMC
- PMC8265154
- OpenAlex
- W3035408224
- Cited by
- 932
- References
- 66
- Citations by year
- 2026: 213 · 2025: 159 · 2024: 142 · 2023: 134 · 2022: 159 · 2021: 98 · 2020: 23 · 2019: 1
- FWCI
- 60.98
- Type
- journal article
- Language
- en
- Update notices
- correction (2020-10-20)
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Repository@Nottingham (University of Nottingham) (acceptedVersion, other-oa)
- Repository@Nottingham (University of Nottingham) (submittedVersion, cc-by)
- Institutional Research Information System (Università degli Studi di Trento) (submittedVersion, other-oa)
- Research Portal (King's College London) (submittedVersion, other-oa)
- Lund University Publications (Lund University) (submittedVersion)
- Funders
- Massachusetts General Hospital
- Wellcome Trust (212904, 212904/Z/18/Z, MR/M016560/1, 212904/Z/18/)
- National Institute for Health and Care Research (MR/M016560/1)
- British Heart Foundation (MR/M016560/1, SP/14/8/31352)
- King's College London
- University of Oxford
- European Commission (CoG-2015_681742_NASCENT, MR/M016560/1, 212904/Z/18/Z)
- Stiftelsen för Strategisk Forskning (CoG-2015_681742_NASCENT)
- Novo Nordisk
- Vetenskapsrådet (CoG-2015_681742_NASCENT)
- National Institutes of Health (UL1TR002541, 1u01ca230551-01a1)
- Medical Research Council (MR/M016560/1, 212904/Z/18/Z, MR/N01183X/1)
- Biotechnology and Biological Sciences Research Council (BB/NO12739/1)
- NIHR Nottingham Biomedical Research Centre
- National Cancer Institute (U01 CA230551, R01CA230551)
- National Center for Advancing Translational Sciences
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Nutrition, Genetics, and Disease
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diet, Metabolism, and Disease
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Keywords
- personalized nutrition, postprandial lipemia, postprandial blood glucose, blood triglycerides, insulin, gut microbiota, interindividual variability, genetic variants, machine learning prediction models, personalized diet, cardiometabolic risk
- MeSH terms
- Gastrointestinal Microbiome, Machine Learning, Nutrients, Adolescent, Adult, Aged, Blood Glucose, C-Peptide, Dietary Carbohydrates, Dietary Fats, Dietary Fiber, Dietary Proteins, Female, Glucose Tolerance Test, Humans, Individuality, Insulin, Male, Middle Aged, Triglycerides, Genetic Variation, Postprandial Period, Polymorphism, Single Nucleotide, Young Adult, Precision Medicine, Healthy Volunteers
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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