Evidence map›Paper›PMID 33296468›Full record

ArticleThe Journal of nutrition2021

Plasma Metabolomic Profiles of Glycemic Index, Glycemic Load, and Carbohydrate Quality Index in the PREDIMED Study.

Mònica Bulló, Christopher Papandreou, Miguel Ruiz-Canela, Marta Guasch-Ferré, Jun Li, Pablo Hernández-Alonso, Estefania Toledo, Liming Liang, Cristina Razquin, Dolores Corella and 11 more

Open access · bronzeAbstract read
In one paragraph

Article in The Journal of nutrition, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
0.9field-weighted citation impact, top 26% 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

13 citing papers in PubMed, 18 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

21 authors at 6 institutions in 2 countries.

Mònica BullóUniversitat Rovira i Virgili, Biochemistry and Biotechnology Department, Human Nutrition Unit, Reus, Spain.
Christopher PapandreouUniversitat Rovira i Virgili, Biochemistry and Biotechnology Department, Human Nutrition Unit, Reus, Spain.
Miguel Ruiz-CanelaCIBER Physiopathology of Obesity and Nutrition (CIBERobn), Institute of Health Carlos III, Madrid, Spain.
Marta Guasch-FerréDepartment of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA.
Jun LiDepartment of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA.
Pablo Hernández-AlonsoUniversitat Rovira i Virgili, Biochemistry and Biotechnology Department, Human Nutrition Unit, Reus, Spain.
Estefania ToledoCIBER Physiopathology of Obesity and Nutrition (CIBERobn), Institute of Health Carlos III, Madrid, Spain.
Liming LiangDepartment of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA.
Cristina RazquinCIBER Physiopathology of Obesity and Nutrition (CIBERobn), Institute of Health Carlos III, Madrid, Spain.
Dolores CorellaCIBER Physiopathology of Obesity and Nutrition (CIBERobn), Institute of Health Carlos III, Madrid, Spain.
Ramon EstruchCIBER Physiopathology of Obesity and Nutrition (CIBERobn), Institute of Health Carlos III, Madrid, Spain.
Emilio RosCIBER Physiopathology of Obesity and Nutrition (CIBERobn), Institute of Health Carlos III, Madrid, Spain.
Montserrat FitóCIBER Physiopathology of Obesity and Nutrition (CIBERobn), Institute of Health Carlos III, Madrid, Spain.
Fernando ArósCIBER Physiopathology of Obesity and Nutrition (CIBERobn), Institute of Health Carlos III, Madrid, Spain.
Miquel FiolCIBER Physiopathology of Obesity and Nutrition (CIBERobn), Institute of Health Carlos III, Madrid, Spain.
Lluís Serra-MajemCIBER Physiopathology of Obesity and Nutrition (CIBERobn), Institute of Health Carlos III, Madrid, Spain.
Clary B ClishBroad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA.
Nerea Becerra-TomásUniversitat Rovira i Virgili, Biochemistry and Biotechnology Department, Human Nutrition Unit, Reus, Spain.
Miguel A Martínez-GonzálezCIBER Physiopathology of Obesity and Nutrition (CIBERobn), Institute of Health Carlos III, Madrid, Spain.
Frank B HuDepartment of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA.
Jordi Salas-SalvadóUniversitat Rovira i Virgili, Biochemistry and Biotechnology Department, Human Nutrition Unit, Reus, Spain.
Instituto de Salud Carlos III · ESHarvard University · USBrigham and Women's Hospital · USBroad Institute · USUniversidad de Las Palmas de Gran Canaria · ESUniversitat de València · ES

Funding

ROLE OF DIETARY CONSTITUENTS ON GENE EXPRESSION IN INTESTINAL EPITHELIUMP30DK040561 · NIDDK · MASSACHUSETTS GENERAL HOSPITAL · PI Elizabeth Austen Lawson, Takara Leah Stanley · 1994 to 2026
$31.6M
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
NHLBI NIH HHS R01 HL118264NIDDK NIH HHS P30 DK040561NIDDK NIH HHS R01 DK102896
6 · The paper itself

Abstract

backgroundThe quality of carbohydrate consumed, assessed by the glycemic index (GI), glycemic load (GL), or carbohydrate quality index (CQI), affects the postprandial glycemic and insulinemic responses, which have been implicated in the etiology of several chronic diseases. However, it is unclear whether plasma metabolites involved in different biological pathways could provide functional insights into the role of carbohydrate quality indices in health.

objectivesWe aimed to identify plasma metabolomic profiles associated with dietary GI, GL, and CQI.

methodsThe present study is a cross-sectional analysis of 1833 participants with overweight/obesity (mean age = 67 y) from 2 case-cohort studies nested within the PREDIMED (Prevención con Dieta Mediterránea) trial. Data extracted from validated FFQs were used to estimate the GI, GL, and CQI. Plasma concentrations of 385 metabolites were profiled with LC coupled to MS and associations of these metabolites with those indices were assessed with elastic net regression analyses.

resultsA total of 58, 18, and 57 metabolites were selected for GI, GL, and CQI, respectively. Choline, cotinine, γ-butyrobetaine, and 36:3 phosphatidylserine plasmalogen were positively associated with GI and GL, whereas they were negatively associated with CQI. Fructose-glucose-galactose was negatively and positively associated with GI/GL and CQI, respectively. Consistent associations of 21 metabolites with both GI and CQI were found but in opposite directions. Negative associations of kynurenic acid, 22:1 sphingomyelin, and 38:6 phosphatidylethanolamine, as well as positive associations of 32:1 phosphatidylcholine with GI and GL were also observed. Pearson correlation coefficients between GI, GL, and CQI and the metabolomic profiles were 0.30, 0.22, and 0.27, respectively.

conclusionsThe GI, GL, and CQI were associated with specific metabolomic profiles in a Mediterranean population at high cardiovascular disease risk. Our findings may help in understanding the role of dietary carbohydrate indices in the development of cardiometabolic disorders. This trial was registered at isrctn.com as ISRCTN35739639.

Indexed as

DietDietary CarbohydratesGlycemic IndexGlycemic LoadMetabolomicsAgedBiomarkersCross-Sectional StudiesFemaleHumansMaleMiddle AgedBiomarkersDietary Carbohydratescarbohydrate quality indexglycemic indexglycemic loadmetabolomicsPREDIMED

Identifiers

PMID33296468
PMCPMC7779218
OpenAlexW3112522956

What OpenQuestion holds

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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.