» Articles » PMID: 35754192

Development and Validation of a Metabolite Score for Red Meat Intake: an Observational Cohort Study and Randomized Controlled Dietary Intervention

Overview
Journal Am J Clin Nutr
Publisher Elsevier
Date 2022 Jun 27
PMID 35754192
Authors
Affiliations
Soon will be listed here.
Abstract

Background: Self-reported meat consumption is associated with disease risk but objective assessment of different dimensions of this heterogeneous dietary exposure in observational and interventional studies remains challenging.

Objectives: We aimed to derive and validate scores based on plasma metabolites for types of meat consumption. For the most predictive score, we aimed to test whether the included metabolites varied with change in meat consumption, and whether the score was associated with incidence of type 2 diabetes (T2D) and other noncommunicable diseases.

Methods: We derived scores based on 781 plasma metabolites for red meat, processed meat, and poultry consumption assessed with 7-d food records among 11,432 participants in the EPIC-Norfolk (European Prospective Investigation into Cancer and Nutrition-Norfolk) cohort. The scores were then tested for internal validity in an independent subset (n = 853) of the same cohort. In focused analysis on the red meat metabolite score, we examined whether the metabolites constituting the score were also associated with meat intake in a randomized crossover dietary intervention trial of meat (n = 12, Lyon, France). In the EPIC-Norfolk study, we assessed the association of the red meat metabolite score with T2D incidence (n = 1478) and other health endpoints.

Results: The best-performing score was for red meat, comprising 139 metabolites which accounted for 17% of the explained variance of red meat consumption in the validation set. In the intervention, 11 top-ranked metabolites in the red meat metabolite score increased significantly after red meat consumption. In the EPIC-Norfolk study, the red meat metabolite score was associated with T2D incidence (adjusted HR per SD: 1.17; 95% CI: 1.10, 1.24).

Conclusions: The red meat metabolite score derived and validated in this study contains metabolites directly derived from meat consumption and is associated with T2D risk. These findings suggest the potential for objective assessment of dietary components and their application for understanding diet-disease associations.The trial in Lyon, France, was registered at clinicaltrials.gov as NCT03354130.

Citing Articles

Development of metabolic signatures of plant-rich dietary patterns using plant-derived metabolites.

Li Y, Xu Y, Sayec M, Spector T, Steves C, Menni C Eur J Nutr. 2024; 64(1):29.

PMID: 39604558 PMC: 11602792. DOI: 10.1007/s00394-024-03511-x.


Proteomic scores and dietary patterns.

Wareham N Nat Food. 2024; 6(1):6-7.

PMID: 39533123 DOI: 10.1038/s43016-024-01078-8.


Recent advances in precision nutrition and cardiometabolic diseases.

Martinez-Gonzalez M, Planes F, Ruiz-Canela M, Toledo E, Estruch R, Salas-Salvado J Rev Esp Cardiol (Engl Ed). 2024; 78(3):263-271.

PMID: 39357800 PMC: 11875914. DOI: 10.1016/j.rec.2024.09.003.


Lipidome changes due to improved dietary fat quality inform cardiometabolic risk reduction and precision nutrition.

Eichelmann F, Prada M, Sellem L, Jackson K, Salas Salvado J, Razquin Burillo C Nat Med. 2024; 30(10):2867-2877.

PMID: 38992128 PMC: 11485259. DOI: 10.1038/s41591-024-03124-1.


Towards nutrition with precision: unlocking biomarkers as dietary assessment tools.

Cuparencu C, Bulmus-Tuccar T, Stanstrup J, La Barbera G, Roager H, Dragsted L Nat Metab. 2024; 6(8):1438-1453.

PMID: 38956322 DOI: 10.1038/s42255-024-01067-y.


References
1.
Neuenschwander M, Ballon A, Weber K, Norat T, Aune D, Schwingshackl L . Role of diet in type 2 diabetes incidence: umbrella review of meta-analyses of prospective observational studies. BMJ. 2019; 366:l2368. PMC: 6607211. DOI: 10.1136/bmj.l2368. View

2.
Sumner L, Amberg A, Barrett D, Beale M, Beger R, Daykin C . Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics. 2013; 3(3):211-221. PMC: 3772505. DOI: 10.1007/s11306-007-0082-2. View

3.
Bunea F, She Y, Ombao H, Gongvatana A, Devlin K, Cohen R . Penalized least squares regression methods and applications to neuroimaging. Neuroimage. 2010; 55(4):1519-27. PMC: 5485905. DOI: 10.1016/j.neuroimage.2010.12.028. View

4.
Pan A, Sun Q, Bernstein A, Schulze M, Manson J, Willett W . Red meat consumption and risk of type 2 diabetes: 3 cohorts of US adults and an updated meta-analysis. Am J Clin Nutr. 2011; 94(4):1088-96. PMC: 3173026. DOI: 10.3945/ajcn.111.018978. View

5.
Godfray H, Aveyard P, Garnett T, Hall J, Key T, Lorimer J . Meat consumption, health, and the environment. Science. 2018; 361(6399). DOI: 10.1126/science.aam5324. View