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. 2023 Jun 5;15(11):2638.
doi: 10.3390/nu15112638.

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

Affiliations

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

Kate M Bermingham et al. Nutrients. .

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.

Keywords: lipids; lipoproteins; nuclear magnetic resonance (NMR).

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Conflict of interest statement

A.M.V., P.W.F., T.D.S. and S.E.B. are consultants to ZOE Ltd. J.W., G.H. and T.D.S. are cofounders of ZOE Ltd. A.M.V., P.W.F., T.D.S., S.E.B., J.W. and G.H. receive options from ZOE Ltd. I.L. is employed by ZOE Ltd. Other authors have no conflict of interest to declare.

Figures

Figure 1
Figure 1
ZOE PREDICT 1 Study Design. Participants arrived fasted for their baseline visit and were given a standardised breakfast (0 h, metabolic challenge meal, 86 g carbohydrate, 53 g fat) and lunch (4 h, 71 g carbohydrate, 22 g fat). Concentrations of glucose, TG and NMR metabolites were determined from venous blood collected at multiple timepoints postprandially. Anthropometric and fasting biochemistry measurements were also measured.
Figure 2
Figure 2
Inter-individual variation and distribution for traditional clinical metabolites and lipoprotein particle size. Fasting and postprandial concentrations of (A) triglycerides (TG) (mmol/L), (B) glucose (mmol/L), (C) non-high-density lipoprotein (HDL) (mmol/L) and particle sizes of: (D) very low-density lipoprotein (VLDL) (nm), (E) low-density lipoprotein (LDL) (nm), (F) high-density lipoprotein (HDL) (nm). n = 1002. Red lines show the median value.
Figure 3
Figure 3
Metabolites with the greatest postprandial change and/or postprandial inter-individual variability. Fasting and postprandial concentrations of (A) triglycerides in extremely large VLDL particles and chylomicrons (TG in XXL VLDL), (B) triglycerides in large HDL particles and chylomicrons (TG in XL HDL), (C) triglycerides in LDL particles and chylomicrons (TG in LDL), (D) pyruvate, (E) mono-unsaturated fatty acids (MUFA), (F) isoleucine. n = 1002. Red lines show the median value.

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