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.

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Fig. 1: Experimental design.
Fig. 2: Variation in postprandial responses.
Fig. 3: Relationship of baseline values, genetic and microbiome factors with postprandial responses.
Fig. 4: Machine-learning models fitted to postprandial measures.
Fig. 5: Associations between fasting and postprandial values for triglyceride, C-peptide and glucose concentrations with clinical measures in the UK cohort.
Fig. 6: Person-specific diversity in postprandial response.

Data availability

The data used for analysis in this study are held by the department of Twin Research at Kings College London. The data can be released to bona fide researchers using our normal procedures overseen by the Wellcome Trust and its guidelines as part of our core funding. We receive around 100 requests per year for our datasets and have a meeting 3 times per month with independent members to assess proposals. The application is at https://twinsuk.ac.uk/resources-for-researchers/access-our-data/. This means that the data need to be anonymized and conform to GDPR standards. Specifically for this paper, all the variables used in the models can be requested as well as the summary outcome measures for each person. The 16S microbiome data will be uploaded onto the EBI website (https://www.ebi.ac.uk/) with unlimited access. Source data for Figs. 26 and Extended Data Figs. 24 are presented with the paper

Code availability

The scripts for statistical analysis are freely available upon request to the Department of Twins Research and Genetic Epidemiology at King’s College London. Application is via https://twinsuk.ac.uk/resources-for-researchers/access-our-data/. The scripts used for machine-learning analyses can be found in the Supplementary Software.

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Acknowledgements

We express our sincere thanks to the participants of the PREDICT 1 study. We thank M. McCarthy (University of Oxford) and L. Hodson (University of Oxford) for valuable feedback on the manuscript. We thank the staff of Z. Global, the Department of Twin Research and Massachusetts General Hospital, for their tireless work in contributing to the running of the study and data collection. We thank Abbott for their support with their CGMs. This work was supported by Zoe Global and also received support from grants from the Wellcome Trust (212904/Z/18/Z) and the Medical Research Council (MRC)/British Heart Foundation Ancestry and Biological Informative Markers for Stratification of Hypertension (AIMHY; MR/M016560/1). S.E.B. was supported in part by a grant funded by the BBSRC (BB/NO12739/1). P.W.F. was supported in part by grants from the European Research Council (CoG-2015_681742_NASCENT), Swedish Research Council, Novo Nordisk Foundation and the Swedish Foundation for Strategic Research (IRC award). A.M.V. was supported by the National Institute for Health Research Nottingham Biomedical Research Centre. TwinsUK is funded by the Wellcome Trust, Medical Research Council, European Union, Chronic Disease Research Foundation (CDRF), Zoe Global and the National Institute for Health Research (NIHR)-funded BioResource, Clinical Research Facility and Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust in partnership with King’s College London. J.M. was supported by a fellowship funded by the European Commission Horizon 2020 program (H2020-MSCA-IF-2015-703787) and by National Institutes of Health grant P30 DK40561. N.S. received support from the European Research Council (ERC-STG project MetaPG), the European H2020 program (ONCOBIOME-825410 project and MASTER-818368 project), and the National Cancer Institute of the National Institutes of Health (1U01CA230551).

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Contributions

Obtained funding: J.W., G.H., T.D.S. Study design and developed concept: S.E.B., A.M.V., J.W., G.H., R.D., A.T.C., N.S., P.W., P.W.F., T.D.S. Data collection: S.E.B., D.A.D., H.A.K., M. Mangino, J.M., I.L., D.H. Data analysis: A.M.V., M. Mangino., F.A., R.D., J.C., C.B., S.G., E.B., M. Mazidi, P.W., N.S. Study coordination: S.E.B., H.A.K., D.A.D., G.H., J.W. Writing the manuscript: S.E.B., A.M.V., D.A.D., M. Mazidi, J.W., J.C., I.L., J.M.O., C.D.G., L.M.D., A.T.C., N.S., P.W.F., T.D.S. All authors reviewed and revised the final manuscript.

Corresponding authors

Correspondence to Ana M. Valdes or Tim D. Spector.

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Competing interests

T.D.S., S.E.B., A.M.V., F.A., P.W.F., L.M.D. and N.S. are consultants to Zoe Global Ltd (‘Zoe’). J.W., G.H., R.D., H.A.K., J.C., C.B., S.G., E.B., P.W. and I.L. are or have been employees of Zoe. Other authors have no conflict of interest to declare.

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Peer review information Jennifer Sargent was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Consort Diagrams for UK and US populations in the PREDICT 1 study.

Consort Diagrams for a, UK and b, US populations in the PREDICT 1 study.

Extended Data Fig. 2 Repeatability in the PREDICT 1 study.

Repeatability in the PREDICT 1 study. a, Intraclass correlations. b, Pearson’s correlation and CV of 2h-iAUCs measured with two monitors worn by the same participant (n = 377). P-value from two-sided t-test c, Mean and standard error of fasting and postprandial serum insulin and C-peptide concentrations during the clinic visit in the PREDICT 1 study, n = 1,036. Source data

Extended Data Fig. 3 Frequency distribution of in-person ranking for 6 of meals shown in Fig. 6a.

Frequency distribution of in-person ranking for 6 of meals shown in Fig. 6a. (High fat 40 g = meal 7, High protein = meal 8, UK average = meal 2, High carb = meal 4, OGTT = meal 5, Uk average at lunch = meal 2). n = 1102 participants. Source data

Extended Data Fig. 4 Machine learning comparisons, cross validation and repeatability.

Machine learning comparisons, cross validation and repeatability. a, Bland-Altman plots comparing predicted and measured postprandial responses in TG, glucose and C-peptide using UK and US data. Sample sizes used were (n = number of meals) triglyceride UK: n = 958 US: n = 91; C-peptide UK: n = 957 US: n = 93; Glucose UK: n = 11550 US: n = 1200. b, Leave-one-out cross-validated Pearson R scores in PREDICT UK. 5-fold cross validation for triglyceride6h-rise on n = 958 meals, for lgucoseiAUC0-2h on n = 11,550 meals, p-values shown for two-sided t-test c, comparison of models using repeat meals vs not using them. Source data

Supplementary information

Reporting Summary

Supplementary Software

Scripts used for data analysis and machine-learning model.

Supplementary Tables 1–5

Supplementary Tables 1–5.

Source data

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Source Data Extended Data Fig. 2

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Berry, S.E., Valdes, A.M., Drew, D.A. et al. Human postprandial responses to food and potential for precision nutrition. Nat Med 26, 964–973 (2020). https://doi.org/10.1038/s41591-020-0934-0

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