Fasting plasma metabolites reflecting meat consumption and their associations with incident type 2 diabetes in two Swedish cohorts.

Red meat biomarkers diabetes mellitus metabolomics processed meat

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:
23 Feb 2024
Historique:
received: 17 10 2023
revised: 02 02 2024
accepted: 20 02 2024
medline: 26 2 2024
pubmed: 26 2 2024
entrez: 25 2 2024
Statut: aheadofprint

Résumé

Consumption of processed red meat has been associated with increased risk of developing type 2 diabetes (T2D), but challenges in dietary assessment call for objective intake biomarkers. To investigate metabolite biomarkers of meat intake and their associations with T2D risk. Fasting plasma samples were collected from a case-control study nested within Västerbotten Intervention Program (VIP) (214 females and 189 males) who developed T2D after a median follow up of 7 years. Panels of biomarker candidates reflecting the consumption of total, processed and unprocessed red meat, and poultry were selected from the untargeted metabolomics data collected on the controls. Observed associations were then replicated in Swedish Mammography clinical subcohort in Uppsala (SMCC) (n=4457 females). Replicated metabolites were assessed for potential association with T2D risk using multivariable conditional logistic regression in the discovery and Cox regression in the replication cohorts. In total, 15 metabolites were associated with at least one meat group in both cohorts. Acylcarnitines 8:1, 8:2, 10:3, reflecting higher processed meat intake (r>0.22, FDR<0.001 for VIP and r>0.05, FDR <0.001 for SMCC) were consistently associated with higher T2D risk in both datasets. Conversely, lysophosphatidylcholine (LPC) 17:1 and phosphatidylcholine (PC) 15:0/18:2 were associated with lower processed meat intake (r < -0.12, FDR <0.023 for VIP and r < -0.05, FDR<0.001 for SMCC) and with lower T2D risk in both datasets, except for PC 15:0/18:2 which was significant only in VIP. All associations were attenuated after adjustment for BMI. Consistent associations of biomarker candidates involved in lipid metabolism between higher processed red meat intake with higher T2D risk and between those reflecting lower intake with the lower risk may suggest a relationship between processed meat intake and higher T2D risk. However, attenuated associations after adjusting for BMI indicates that such a relationship may at least partly be mediated or confounded by BMI.

Sections du résumé

BACKGROUND BACKGROUND
Consumption of processed red meat has been associated with increased risk of developing type 2 diabetes (T2D), but challenges in dietary assessment call for objective intake biomarkers.
AIMS OBJECTIVE
To investigate metabolite biomarkers of meat intake and their associations with T2D risk.
METHODS METHODS
Fasting plasma samples were collected from a case-control study nested within Västerbotten Intervention Program (VIP) (214 females and 189 males) who developed T2D after a median follow up of 7 years. Panels of biomarker candidates reflecting the consumption of total, processed and unprocessed red meat, and poultry were selected from the untargeted metabolomics data collected on the controls. Observed associations were then replicated in Swedish Mammography clinical subcohort in Uppsala (SMCC) (n=4457 females). Replicated metabolites were assessed for potential association with T2D risk using multivariable conditional logistic regression in the discovery and Cox regression in the replication cohorts.
RESULTS RESULTS
In total, 15 metabolites were associated with at least one meat group in both cohorts. Acylcarnitines 8:1, 8:2, 10:3, reflecting higher processed meat intake (r>0.22, FDR<0.001 for VIP and r>0.05, FDR <0.001 for SMCC) were consistently associated with higher T2D risk in both datasets. Conversely, lysophosphatidylcholine (LPC) 17:1 and phosphatidylcholine (PC) 15:0/18:2 were associated with lower processed meat intake (r < -0.12, FDR <0.023 for VIP and r < -0.05, FDR<0.001 for SMCC) and with lower T2D risk in both datasets, except for PC 15:0/18:2 which was significant only in VIP. All associations were attenuated after adjustment for BMI.
CONCLUSION CONCLUSIONS
Consistent associations of biomarker candidates involved in lipid metabolism between higher processed red meat intake with higher T2D risk and between those reflecting lower intake with the lower risk may suggest a relationship between processed meat intake and higher T2D risk. However, attenuated associations after adjusting for BMI indicates that such a relationship may at least partly be mediated or confounded by BMI.

Identifiants

pubmed: 38403167
pii: S0002-9165(24)00157-6
doi: 10.1016/j.ajcnut.2024.02.012
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Stefania Noerman (S)

Department of Life Sciences, Division of Food and Nutrition Science, Chalmers University of Technology, Gothenburg, Sweden. Electronic address: noerman@chalmers.se.

Anna Johansson (A)

Department of Life Sciences, Division of Food and Nutrition Science, Chalmers University of Technology, Gothenburg, Sweden.

Lin Shi (L)

Department of Life Sciences, Division of Food and Nutrition Science, Chalmers University of Technology, Gothenburg, Sweden; School of Food Engineering and Nutritional Science, Shaanxi Normal University, Xi'an, China.

Marko Lehtonen (M)

School of Pharmacy, University of Eastern Finland, Kuopio, Finland.

Kati Hanhineva (K)

Department of Life Technologies, Food Sciences Unit, University of Turku, Turku, Finland; Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland.

Ingegerd Johansson (I)

Department of Odontology, School of Dentistry, Cariology, Umeå University, Sweden.

Carl Brunius (C)

Department of Life Sciences, Division of Food and Nutrition Science, Chalmers University of Technology, Gothenburg, Sweden.

Rikard Landberg (R)

Department of Life Sciences, Division of Food and Nutrition Science, Chalmers University of Technology, Gothenburg, Sweden.

Classifications MeSH