Evidence map›Paper›PMID 36214069›Full record

ArticleMolecular nutrition & food research2022

Plasma Metabolite Profiles Associated with the Amount and Source of Meat and Fish Consumption and the Risk of Type 2 Diabetes.

Jesús García-Gavilán, Stephanie K Nishi, Indira Paz-Graniel, Marta Guasch-Ferré, Cristina Razquin, Clary B Clish, Estefanía Toledo, Miguel Ruiz-Canela, Dolores Corella, Amy Deik and 13 more

Open access · bronzeAbstract read
In one paragraph

Article in Molecular nutrition & food research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.0field-weighted citation impact, top 25% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 12 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

23 authors at 9 institutions in 4 countries.

Jesús García-GavilánDepartament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana, Hospital Universitari San Joan de Reus, Universitat Rovira i Virgili, Reus, 43202, Spain.ORCID 0000-0002-3707-5255
Stephanie K NishiDepartament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana, Hospital Universitari San Joan de Reus, Universitat Rovira i Virgili, Reus, 43202, Spain.ORCID 0000-0002-7878-5368
Indira Paz-GranielDepartament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana, Hospital Universitari San Joan de Reus, Universitat Rovira i Virgili, Reus, 43202, Spain.
Marta Guasch-FerréDepartment of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, 02115, USA.ORCID 0000-0001-8525-1404
Cristina RazquinConsorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, 28029, Spain.
Clary B ClishThe Broad Institute of Harvard and MIT, Boston, MA, 02142, USA.
Estefanía ToledoConsorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, 28029, Spain.
Miguel Ruiz-CanelaConsorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, 28029, Spain.ORCID 0000-0002-7684-2787
Dolores CorellaConsorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, 28029, Spain.
Amy DeikThe Broad Institute of Harvard and MIT, Boston, MA, 02142, USA.
Jean-Philippe Drouin-ChartierCentre Nutrition, Santé et Société, Institut sur la Nutrition et les Aliments Fonctionnels, Faculté de Pharmacie, Université Laval, Québec, G1V 0A6, Canada.
Clemens WittenbecherDepartment of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, 02115, USA.
Nancy BabioDepartament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana, Hospital Universitari San Joan de Reus, Universitat Rovira i Virgili, Reus, 43202, Spain.
Ramon EstruchConsorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, 28029, Spain.
Emilio RosConsorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, 28029, Spain.
Montserrat FitóConsorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, 28029, Spain.
Fernando ArósConsorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, 28029, Spain.
Miquel FiolConsorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, 28029, Spain.
Lluís Serra-MajemConsorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, 28029, Spain.
Liming LiangDepartment of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA.
Miguel A Martínez-GonzálezConsorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, 28029, Spain.
Frank B HuDepartment of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, 02115, USA.
Jordi Salas-SalvadóDepartament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana, Hospital Universitari San Joan de Reus, Universitat Rovira i Virgili, Reus, 43202, Spain.ORCID 0000-0003-2700-7459
Instituto de Salud Carlos III · ESBrigham and Women's Hospital · USBroad Institute · USMassachusetts Department of Public Health · USHarvard University · USSt. Michael's Hospital · CAUniversidad de Las Palmas de Gran Canaria · ESUniversitat de València · ESUniversité Laval · CA

Funding

Mediterranean diet, Metabolites, and Cardiovascular DiseaseR01HL118264 · NHLBI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI HU, FRANK B, LAMUELA-RAVENTÓS, ROSA · 2013 to 2025
$6.8M
Dietary Interventions, Metabolites, and Risk of Type 2 DiabetesR01DK102896 · NIDDK · HARVARD SCHOOL OF PUBLIC HEALTH · PI HU, FRANK B, MARTINEZ GONZALEZ, MIGUEL A. · 2014 to 2017
$1.9M
CIHR MFE-171207NHLBI NIH HHS R01 HL118264NIDDK NIH HHS R01 DK102896
6 · The paper itself

Abstract

scopeConsumption of meat has been associated with a higher risk of type 2 diabetes (T2D), but if plasma metabolite profiles associated with these foods reflect this relationship is unknown. The objective is to identify a metabolite signature of consumption of total meat (TM), red meat (RM), processed red meat (PRM), and fish and examine if they are associated with T2D risk. METHODS AND

resultsThe discovery population includes 1833 participants from the PREDIMED trial. The internal validation sample includes 1522 participants with available 1-year follow-up metabolomic data. Associations between metabolites and TM, RM, PRM, and fish are evaluated with elastic net regression. Associations between the profiles and incident T2D are estimated using Cox regressions. The profiles included 72 metabolites for TM, 69 for RM, 74 for PRM, and 66 for fish. After adjusting for T2D risk factors, only profiles of TM (Hazard Ratio (HR): 1.25, 95% CI: 1.06-1.49), RM (HR: 1.27, 95% CI: 1.07-1.52), and PRM (HR: 1.27, 95% CI: 1.07-1.51) are associated with T2D.

conclusionsThe consumption of TM, its subtypes, and fish is associated with different metabolites, some of which have been previously associated with T2D. Scores based on the identified metabolites for TM, RM, and PRM show a significant association with T2D risk.

Indexed as

Diabetes Mellitus, Type 2Red MeatAnimalsDietFishesMeatRisk FactorsfishmeatmetabolomicsPREDIMEDtype 2 diabetes

Identifiers

PMID36214069
PMCPMC9722604
OpenAlexW4303986501

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.