Characterisation of Fasting and Postprandial NMR Metabolites: Insights from the ZOE PREDICT 1 Study.
lipids
lipoproteins
nuclear magnetic resonance (NMR)
Journal
Nutrients
ISSN: 2072-6643
Titre abrégé: Nutrients
Pays: Switzerland
ID NLM: 101521595
Informations de publication
Date de publication:
05 Jun 2023
05 Jun 2023
Historique:
received:
12
04
2023
revised:
12
05
2023
accepted:
18
05
2023
medline:
12
6
2023
pubmed:
10
6
2023
entrez:
10
6
2023
Statut:
epublish
Résumé
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. In the ZOE PREDICT 1 study ( 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. 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.
Sections du résumé
BACKGROUND
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
METHODS
In the ZOE PREDICT 1 study (
RESULTS
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
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.
Identifiants
pubmed: 37299601
pii: nu15112638
doi: 10.3390/nu15112638
pmc: PMC10255657
pii:
doi:
Substances chimiques
Blood Glucose
0
Ketone Bodies
0
Lipoproteins
0
Triglycerides
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Wellcome Trust
ID : 212904/Z/18/Z
Pays : United Kingdom
Références
Lipids Health Dis. 2011 Oct 18;10:181
pubmed: 22008512
Circulation. 2007 Jan 30;115(4):450-8
pubmed: 17190864
J Nutr. 2019 Mar 1;149(3):422-431
pubmed: 30759235
Circ Cardiovasc Genet. 2015 Feb;8(1):192-206
pubmed: 25691689
J Acad Nutr Diet. 2018 Sep;118(9):1591-1602
pubmed: 30146071
PLoS One. 2015 Aug 14;10(8):e0135437
pubmed: 26274920
PLoS One. 2011;6(6):e21103
pubmed: 21698256
Br J Nutr. 2015 Jul 14;114(1):98-107
pubmed: 26004166
Clin Chem. 2021 Jan 8;67(1):276-287
pubmed: 33409531
Front Physiol. 2019 Jul 16;10:856
pubmed: 31379592
Eur J Clin Nutr. 2003 Dec;57(12):1536-44
pubmed: 14647218
BMJ Open. 2014 Mar 27;4(3):e004503
pubmed: 24674997
Hypertension. 2015 Aug;66(2):422-9
pubmed: 26034203
Nat Med. 2021 Feb;27(2):321-332
pubmed: 33432175
J Bone Miner Res. 2018 Apr;33(4):643-650
pubmed: 29232479
Nat Commun. 2019 Aug 20;10(1):3346
pubmed: 31431621
Int J Obes (Lond). 2016 Feb;40(2):305-11
pubmed: 26278005
Cancer Epidemiol Biomarkers Prev. 2013 Apr;22(4):631-40
pubmed: 23396963
Nat Commun. 2023 Feb 3;14(1):604
pubmed: 36737450
PLoS One. 2014 May 01;9(5):e95749
pubmed: 24788433
Clin Nutr. 2021 Jul;40(7):4762-4771
pubmed: 34242916
PLoS One. 2017 Feb 21;12(2):e0172732
pubmed: 28222178
J Comp Physiol B. 2009 Jan;179(1):1-56
pubmed: 18597096
Am J Clin Nutr. 2021 May 8;113(5):1221-1231
pubmed: 33675343
Circulation. 2015 Mar 3;131(9):774-85
pubmed: 25573147
Hum Reprod. 2010 Apr;25(4):949-56
pubmed: 20150174
J Clin Lipidol. 2022 May-Jun;16(3):250-252
pubmed: 35292206
J Am Heart Assoc. 2021 Dec 7;10(23):e021995
pubmed: 34845932
Metabolism. 1999 Mar;48(3):301-7
pubmed: 10094104
Diabetes. 2013 Dec;62(12):4270-6
pubmed: 23884885
J Clin Lipidol. 2020 Mar - Apr;14(2):241-251
pubmed: 32205068
Curr Vasc Pharmacol. 2011 May;9(3):258-70
pubmed: 21314632
Twin Res Hum Genet. 2013 Feb;16(1):144-9
pubmed: 23088889
Clin Biochem. 2021 Jan;87:85-92
pubmed: 33159964
Nat Med. 2020 Jun;26(6):964-973
pubmed: 32528151
J Clin Endocrinol Metab. 2019 Dec 1;104(12):6357-6370
pubmed: 31390012
Arch Public Health. 2015 Sep 25;73:43
pubmed: 26413295
Obes Rev. 2012 Oct;13(10):923-84
pubmed: 22780564
Am J Clin Nutr. 2021 Sep 1;114(3):1028-1038
pubmed: 34100082
Mol Syst Biol. 2011 Aug 30;7:525
pubmed: 21878913