Plasma metabolomics profiles suggest beneficial effects of a low-glycemic load dietary pattern on inflammation and energy metabolism.
crossover
dietary intervention
dietary patterns
glycemic load
inflammation
insulin resistance
metabolomics
whole grains
Journal
The American journal of clinical nutrition
ISSN: 1938-3207
Titre abrégé: Am J Clin Nutr
Pays: United States
ID NLM: 0376027
Informations de publication
Date de publication:
01 10 2019
01 10 2019
Historique:
received:
16
04
2019
accepted:
02
07
2019
pubmed:
23
8
2019
medline:
24
3
2020
entrez:
22
8
2019
Statut:
ppublish
Résumé
Low-glycemic load dietary patterns, characterized by consumption of whole grains, legumes, fruits, and vegetables, are associated with reduced risk of several chronic diseases. Using samples from a randomized, controlled, crossover feeding trial, we evaluated the effects on metabolic profiles of a low-glycemic whole-grain dietary pattern (WG) compared with a dietary pattern high in refined grains and added sugars (RG) for 28 d. LC-MS-based targeted metabolomics analysis was performed on fasting plasma samples from 80 healthy participants (n = 40 men, n = 40 women) aged 18-45 y. Linear mixed models were used to evaluate differences in response between diets for individual metabolites. Kyoto Encyclopedia of Genes and Genomes (KEGG)-defined pathways and 2 novel data-driven analyses were conducted to consider differences at the pathway level. There were 121 metabolites with detectable signal in >98% of all plasma samples. Eighteen metabolites were significantly different between diets at day 28 [false discovery rate (FDR) < 0.05]. Inositol, hydroxyphenylpyruvate, citrulline, ornithine, 13-hydroxyoctadecadienoic acid, glutamine, and oxaloacetate were higher after the WG diet than after the RG diet, whereas melatonin, betaine, creatine, acetylcholine, aspartate, hydroxyproline, methylhistidine, tryptophan, cystamine, carnitine, and trimethylamine were lower. Analyses using KEGG-defined pathways revealed statistically significant differences in tryptophan metabolism between diets, with kynurenine and melatonin positively associated with serum C-reactive protein concentrations. Novel data-driven methods at the metabolite and network levels found correlations among metabolites involved in branched-chain amino acid (BCAA) degradation, trimethylamine-N-oxide production, and β oxidation of fatty acids (FDR < 0.1) that differed between diets, with more favorable metabolic profiles detected after the WG diet. Higher BCAAs and trimethylamine were positively associated with homeostasis model assessment-insulin resistance. These exploratory metabolomics results support beneficial effects of a low-glycemic load dietary pattern characterized by whole grains, legumes, fruits, and vegetables, compared with a diet high in refined grains and added sugars on inflammation and energy metabolism pathways. This trial was registered at clinicaltrials.gov as NCT00622661.
Sections du résumé
BACKGROUND
Low-glycemic load dietary patterns, characterized by consumption of whole grains, legumes, fruits, and vegetables, are associated with reduced risk of several chronic diseases.
METHODS
Using samples from a randomized, controlled, crossover feeding trial, we evaluated the effects on metabolic profiles of a low-glycemic whole-grain dietary pattern (WG) compared with a dietary pattern high in refined grains and added sugars (RG) for 28 d. LC-MS-based targeted metabolomics analysis was performed on fasting plasma samples from 80 healthy participants (n = 40 men, n = 40 women) aged 18-45 y. Linear mixed models were used to evaluate differences in response between diets for individual metabolites. Kyoto Encyclopedia of Genes and Genomes (KEGG)-defined pathways and 2 novel data-driven analyses were conducted to consider differences at the pathway level.
RESULTS
There were 121 metabolites with detectable signal in >98% of all plasma samples. Eighteen metabolites were significantly different between diets at day 28 [false discovery rate (FDR) < 0.05]. Inositol, hydroxyphenylpyruvate, citrulline, ornithine, 13-hydroxyoctadecadienoic acid, glutamine, and oxaloacetate were higher after the WG diet than after the RG diet, whereas melatonin, betaine, creatine, acetylcholine, aspartate, hydroxyproline, methylhistidine, tryptophan, cystamine, carnitine, and trimethylamine were lower. Analyses using KEGG-defined pathways revealed statistically significant differences in tryptophan metabolism between diets, with kynurenine and melatonin positively associated with serum C-reactive protein concentrations. Novel data-driven methods at the metabolite and network levels found correlations among metabolites involved in branched-chain amino acid (BCAA) degradation, trimethylamine-N-oxide production, and β oxidation of fatty acids (FDR < 0.1) that differed between diets, with more favorable metabolic profiles detected after the WG diet. Higher BCAAs and trimethylamine were positively associated with homeostasis model assessment-insulin resistance.
CONCLUSIONS
These exploratory metabolomics results support beneficial effects of a low-glycemic load dietary pattern characterized by whole grains, legumes, fruits, and vegetables, compared with a diet high in refined grains and added sugars on inflammation and energy metabolism pathways. This trial was registered at clinicaltrials.gov as NCT00622661.
Identifiants
pubmed: 31432072
pii: S0002-9165(22)01273-4
doi: 10.1093/ajcn/nqz169
pmc: PMC6766441
doi:
Substances chimiques
Biomarkers
0
Banques de données
ClinicalTrials.gov
['NCT00622661']
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
984-992Subventions
Organisme : NHLBI NIH HHS
ID : K01 HL124050
Pays : United States
Organisme : NCI NIH HHS
ID : P30 CA015704
Pays : United States
Organisme : NCI NIH HHS
ID : U54 CA116847
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA192222
Pays : United States
Organisme : NIDDK NIH HHS
ID : P30 DK035816
Pays : United States
Organisme : NIGMS NIH HHS
ID : R01 GM114029
Pays : United States
Informations de copyright
Copyright © American Society for Nutrition 2019.
Références
Stat Appl Genet Mol Biol. 2010;9:Article22
pubmed: 20597848
Bioinformatics. 2016 Oct 15;32(20):3165-3174
pubmed: 27357170
Nutr Rev. 2003 May;61(5 Pt 2):S49-55
pubmed: 12828192
Food Chem. 2014 Jun 15;153:151-6
pubmed: 24491714
Circ Res. 2018 Jan 19;122(2):369-384
pubmed: 29348256
Mol Nutr Food Res. 2019 Jan;63(1):e1700975
pubmed: 29603657
Am J Clin Nutr. 1985 Oct;42(4):639-43
pubmed: 2413754
Database (Oxford). 2010;2010:bap024
pubmed: 20428313
J Neurochem. 1978 Jan;30(1):121-4
pubmed: 621503
J Chiropr Med. 2017 Mar;16(1):10-18
pubmed: 28228693
FASEB J. 2019 Jul;33(7):8033-8042
pubmed: 30925066
Mol Nutr Food Res. 2018 Mar;62(6):e1700756
pubmed: 29377510
Am J Clin Nutr. 2014 Jul;100 Suppl 1:394S-8S
pubmed: 24920034
Annu Rev Med. 2015;66:343-59
pubmed: 25587655
Eur J Clin Nutr. 2018 Jun;72(6):818-825
pubmed: 29563640
Circulation. 2016 Jan 12;133(2):187-225
pubmed: 26746178
Food Funct. 2015 Sep;6(9):2949-56
pubmed: 26165375
Nutrients. 2016 Jul 01;8(7):
pubmed: 27376324
Int J Obes (Lond). 2008 Aug;32 Suppl 3:S56-9
pubmed: 18695655
Methods Enzymol. 2017;585:135-158
pubmed: 28109426
Mol Nutr Food Res. 2019 Jan;63(1):e1800215
pubmed: 30094970
Cell Metab. 2014 Jan 7;19(1):96-108
pubmed: 24411942
Med Hypotheses. 2018 Sep;118:129-138
pubmed: 30037600
J Amino Acids. 2016;2016:8952520
pubmed: 26881063
Analyst. 2015 Apr 21;140(8):2726-34
pubmed: 25699545
J Proteome Res. 2014 Sep 5;13(9):4120-30
pubmed: 25126899
PLoS One. 2018 Jul 13;13(7):e0199351
pubmed: 30005063
Metabolism. 2013 Feb;62(2):188-95
pubmed: 22959497
Diabetol Metab Syndr. 2014 Mar 20;6(1):40
pubmed: 24650495
Biochem Pharmacol. 1999 Sep 15;58(6):1047-55
pubmed: 10509757
Nutrients. 2017 Aug 09;9(8):null
pubmed: 28792455
J Nutr. 2012 Feb;142(2):369-74
pubmed: 22190020
Am J Clin Nutr. 2008 Mar;87(3):627-37
pubmed: 18326601
PLoS One. 2018 Feb 5;13(2):e0192169
pubmed: 29401505
Food Nutr Res. 2012;56:null
pubmed: 22826693
J Comput Biol. 2009 Mar;16(3):407-26
pubmed: 19254181
Eur J Clin Nutr. 2012 Oct;66(10):1146-52
pubmed: 22892437
Nat Med. 2011 Apr;17(4):448-53
pubmed: 21423183
Int J Tryptophan Res. 2017 Mar 15;10:1178646917691938
pubmed: 28469468
J Am Coll Cardiol. 2017 Jul 25;70(4):411-422
pubmed: 28728684
Nutrients. 2018 Oct 01;10(10):null
pubmed: 30275434
J Am Heart Assoc. 2016 Feb 22;5(2):
pubmed: 26903003
MBio. 2016 Apr 05;7(2):e02210-15
pubmed: 27048804
Nucleic Acids Res. 2000 Jan 1;28(1):27-30
pubmed: 10592173
Card Fail Rev. 2018 May;4(1):54-61
pubmed: 29892479
Appetite. 2016 Dec 1;107:253-259
pubmed: 27507131
J Food Sci. 2013 Jun;78 Suppl 1:A18-25
pubmed: 23789932
Am J Clin Nutr. 2009 Dec;90(6):1457-65
pubmed: 19812179
Nucleic Acids Res. 2012 Sep 1;40(17):e133
pubmed: 22638577
Nutr Res. 2018 Jul 10;:
pubmed: 30077352