Untargeted metabolomic analysis investigating links between unprocessed red meat intake and markers of inflammation.

BMI C-reactive protein Red meat adiposity biomarker inflammation metabolome-wide association study metabolomics

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:
11 2023
Historique:
received: 31 01 2023
revised: 21 08 2023
accepted: 30 08 2023
medline: 6 11 2023
pubmed: 4 9 2023
entrez: 3 9 2023
Statut: ppublish

Résumé

Whether red meat consumption is associated with higher inflammation or confounded by increased adiposity remains unclear. Plasma metabolites capture the effects of diet after food is processed, digested, and absorbed, and correlate with markers of inflammation, so they can help clarify diet-health relationships. To identify whether any metabolites associated with red meat intake are also associated with inflammation. A cross-sectional analysis of observational data from older adults (52.84% women, mean age 63 ± 0.3 y) participating in the Multi-Ethnic Study of Atherosclerosis (MESA). Dietary intake was assessed by food-frequency questionnaire, alongside C-reactive protein (CRP), interleukin-2, interleukin-6, fibrinogen, homocysteine, and tumor necrosis factor alpha, and untargeted proton nuclear magnetic resonance ( In analyses that adjust for BMI, neither processed nor unprocessed forms of red meat were associated with any markers of inflammation (all P > 0.01). However, when adjusting for BMI, unprocessed red meat was inversely associated with spectral features representing the metabolite glutamine (sentinel hit: β = -0.09 ± 0.02, P = 2.0 × 10 Our analyses were unable to support a relationship between either processed or unprocessed red meat and inflammation, over and above any confounding by BMI. Glutamine, a plasma correlate of lower unprocessed red meat intake, was associated with lower CRP levels. The differences in diet-inflammation associations, compared with diet metabolite-inflammation associations, warrant further investigation to understand the extent that these arise from the following: 1) a reduction in measurement error with metabolite measures; 2) the extent that which factors other than unprocessed red meat intake contribute to glutamine levels; and 3) the ability of plasma metabolites to capture individual differences in how food intake is metabolized.

Sections du résumé

BACKGROUND
Whether red meat consumption is associated with higher inflammation or confounded by increased adiposity remains unclear. Plasma metabolites capture the effects of diet after food is processed, digested, and absorbed, and correlate with markers of inflammation, so they can help clarify diet-health relationships.
OBJECTIVE
To identify whether any metabolites associated with red meat intake are also associated with inflammation.
METHODS
A cross-sectional analysis of observational data from older adults (52.84% women, mean age 63 ± 0.3 y) participating in the Multi-Ethnic Study of Atherosclerosis (MESA). Dietary intake was assessed by food-frequency questionnaire, alongside C-reactive protein (CRP), interleukin-2, interleukin-6, fibrinogen, homocysteine, and tumor necrosis factor alpha, and untargeted proton nuclear magnetic resonance (
RESULTS
In analyses that adjust for BMI, neither processed nor unprocessed forms of red meat were associated with any markers of inflammation (all P > 0.01). However, when adjusting for BMI, unprocessed red meat was inversely associated with spectral features representing the metabolite glutamine (sentinel hit: β = -0.09 ± 0.02, P = 2.0 × 10
CONCLUSIONS
Our analyses were unable to support a relationship between either processed or unprocessed red meat and inflammation, over and above any confounding by BMI. Glutamine, a plasma correlate of lower unprocessed red meat intake, was associated with lower CRP levels. The differences in diet-inflammation associations, compared with diet metabolite-inflammation associations, warrant further investigation to understand the extent that these arise from the following: 1) a reduction in measurement error with metabolite measures; 2) the extent that which factors other than unprocessed red meat intake contribute to glutamine levels; and 3) the ability of plasma metabolites to capture individual differences in how food intake is metabolized.

Identifiants

pubmed: 37660929
pii: S0002-9165(23)66116-7
doi: 10.1016/j.ajcnut.2023.08.018
pii:
doi:

Substances chimiques

Glutamine 0RH81L854J

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, U.S. Gov't, Non-P.H.S. Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

989-999

Subventions

Organisme : NCATS NIH HHS
ID : UL1 TR001881
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL105756
Pays : United States

Informations de copyright

Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.

Auteurs

Alexis C Wood (AC)

United States Department of Agriculture (USDA)/ARS Children's Nutrition Research Center, Baylor College of Medicine, TX, United States. Electronic address: LekkiWood@Gmail.com.

Goncalo Graca (G)

Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom.

Meghana Gadgil (M)

Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, CA, United States.

Mackenzie K Senn (MK)

United States Department of Agriculture (USDA)/ARS Children's Nutrition Research Center, Baylor College of Medicine, TX, United States.

Matthew A Allison (MA)

Department of Family Medicine, University of California, San Diego, La Jolla, CA, United States.

Ioanna Tzoulaki (I)

Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece; Department of Epidemiology and Biostatistics, Imperial College London School of Public Health, London, United Kingdom.

Philip Greenland (P)

Departments of Preventive Medicine and Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States.

Timothy Ebbels (T)

Biomolecular Medicine, Department of Surgery and Cancer, Imperial College London, London, United Kingdom.

Paul Elliott (P)

Department of Epidemiology and Biostatistics, Imperial College London School of Public Health, London, United Kingdom.

Mark O Goodarzi (MO)

Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States.

Russell Tracy (R)

Laboratory for Clinical Biochemistry Research, University of Vermont, Burlington, VT, United States.

Jerome I Rotter (JI)

The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, United States.

David Herrington (D)

Section on Cardiovascular Medicine, Department of Internal Medicine, Wake Forest School of Medicine; Medical Center Boulevard, Winston-Salem, NC, United States.

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Classifications MeSH