Characterisation of Fasting and Postprandial NMR Metabolites: Insights from the ZOE PREDICT 1 Study

Author:

Bermingham Kate M.12,Mazidi Mohsen234,Franks Paul W.56,Maher Tyler1,Valdes Ana M.78ORCID,Linenberg Inbar19,Wolf Jonathan9,Hadjigeorgiou George9,Spector Tim D.2,Menni Cristina2ORCID,Ordovas Jose M.101112,Berry Sarah E.1,Hall Wendy L.1ORCID

Affiliation:

1. Department of Nutritional Sciences, King’s College London, London WC2R 2LS, UK

2. Department of Twins Research and Genetic Epidemiology, King’s College London, London WC2R 2LS, UK

3. Medical Research Council Population Health Research Unit, University of Oxford, Oxford OX1 3QR, UK

4. Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK

5. Department of Clinical Sciences, Lund University, 21428 Malmö, Sweden

6. Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA

7. School of Medicine, University of Nottingham, Nottingham NG5 1PB, UK

8. Nottingham NIHR Biomedical Research Centre, Nottingham NG7 2UH, UK

9. ZOE Ltd., London SE1 7RW, UK

10. Jean Mayer USDA Human Nutrition Research Centre on Aging (JM-USDA-HNRCA), Tufts University, Boston, MA 02111, USA

11. IMDEA Food Institute, CEI UAM + CSIC, 28049 Madrid, Spain

12. Centro de Investigación Biomédica en Red-Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain

Abstract

Background: Postprandial metabolomic profiles and their inter-individual variability are not well characterised. Here, we describe postprandial metabolite changes, their correlations with fasting values and their inter- and intra-individual variability, following a standardised meal in the ZOE PREDICT 1 cohort. Methods: In the ZOE PREDICT 1 study (n = 1002 (NCT03479866)), 250 metabolites, mainly lipids, were measured by a Nightingale NMR panel in fasting and postprandial (4 and 6 h after a 3.7 MJ mixed nutrient meal, with a second 2.2 MJ mixed nutrient meal at 4 h) serum samples. For each metabolite, inter- and intra-individual variability over time was evaluated using linear mixed modelling and intraclass correlation coefficients (ICC) were calculated. Results: Postprandially, 85% (of 250 metabolites) significantly changed from fasting at 6 h (47% increased, 53% decreased; Kruskal–Wallis), with 37 measures increasing by >25% and 14 increasing by >50%. The largest changes were observed in very large lipoprotein particles and ketone bodies. Seventy-one percent of circulating metabolites were strongly correlated (Spearman’s rho >0.80) between fasting and postprandial timepoints, and 5% were weakly correlated (rho <0.50). The median ICC of the 250 metabolites was 0.91 (range 0.08–0.99). The lowest ICCs (ICC <0.40, 4% of measures) were found for glucose, pyruvate, ketone bodies (β-hydroxybutyrate, acetoacetate, acetate) and lactate. Conclusions: In this large-scale postprandial metabolomic study, circulating metabolites were highly variable between individuals following sequential mixed meals. Findings suggest that a meal challenge may yield postprandial responses divergent from fasting measures, specifically for glycolysis, essential amino acid, ketone body and lipoprotein size metabolites.

Funder

ZOE Ltd.

Wellcome Trust

Medical Research Council

Versus Arthritis

European Union Horizon 2020

Chronic Disease Research Foundation

National Institute for Health Research

Clinical Research Network

Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust in partnership with King’s College London

Scientific Advisory Board

Publisher

MDPI AG

Subject

Food Science,Nutrition and Dietetics

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