Evidence map›Paper›PMID 31432072›Full record

ArticleThe American journal of clinical nutrition2019

Plasma metabolomics profiles suggest beneficial effects of a low-glycemic load dietary pattern on inflammation and energy metabolism.

Sandi L Navarro, Aliasghar Tarkhan, Ali Shojaie, Timothy W Randolph, Haiwei Gu, Danijel Djukovic, Katie J Osterbauer, Meredith A Hullar, Mario Kratz, Marian L Neuhouser and 3 more

Registry-linked trialOpen access · bronzeAbstract read
In one paragraph

Article in The American journal of clinical nutrition, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT00622661 (A Feeding Study to Examine the Effect of Glycemic Load and Obesity on Cancer Biomarkers), which is not on this map. Cited by 28 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 2 pooled it
1.6field-weighted citation impact, top 16% 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.

NCT00622661 nacompletednot on this map

A Feeding Study to Examine the Effect of Glycemic Load and Obesity on Cancer Biomarkers

TypeinterventionalSponsorFred Hutchinson Cancer CenterRan2006 to 2010Enrolled89ConditionsHealthy, OverweightArmsLow Glycemic Load Diet, High Glycemic Load Diet
3 · Its place in the literature

Who cites it

28 citing papers in PubMed, 2 syntheses or guidelines pooled it, 41 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

13 authors at 3 institutions in 1 country.

Sandi L NavarroDivision of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Aliasghar TarkhanDepartment of Biostatistics, University of Washington, Seattle, WA, USA.
Ali ShojaieDivision of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Timothy W RandolphDivision of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Haiwei GuCollege of Health Solutions, Arizona State University, Phoenix, AZ, USA.
Danijel DjukovicNorthwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA, USA.
Katie J OsterbauerDivision of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Meredith A HullarDivision of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Mario KratzDivision of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Marian L NeuhouserDivision of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Paul D LampeDivision of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Daniel RafteryDivision of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Johanna W LampeDivision of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Fred Hutch Cancer Center · USUniversity of Washington · USArizona State University · US

Funding

Translational Bioimaging Core Shared ResourceP30CA015704 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI Eric Collisson · 1985 to 2026
$296.4M
PILOT STUDY--CLINICAL NUTRITION RESEARCHP30DK035816 · NIDDK · UNIVERSITY OF WASHINGTON · PI Ellen A Schur · 1986 to 2026
$30.4M
Preventing obesity in low income working adultsU54CA116847 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI MCTIERNAN, ANNE M. · 2005 to 2010
$12.6M
Human and Bacterial Molecular Pathways in Cancer Risk: Modulation by DietR01CA192222 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI LAMPE, JOHANNA W · 2016 to 2019
$2.9M
Machine Learning Tools for Discovery and Analysis of Active Metabolic PathwaysR01GM114029 · NIGMS · UNIVERSITY OF WASHINGTON · PI SHOJAIE, ALI · 2016 to 2020
$1.7M
Statistical Methods for Network-Based Integrative Analysis of CVD Epigenetic DataK01HL124050 · NHLBI · UNIVERSITY OF WASHINGTON · PI SHOJAIE, ALI · 2016 to 2020
$693k
NCI NIH HHS P30 CA015704NCI NIH HHS R01 CA192222NCI NIH HHS U54 CA116847NHLBI NIH HHS K01 HL124050NIDDK NIH HHS P30 DK035816NIGMS NIH HHS R01 GM114029
6 · The paper itself

Abstract

backgroundLow-glycemic load dietary patterns, characterized by consumption of whole grains, legumes, fruits, and vegetables, are associated with reduced risk of several chronic diseases.

methodsUsing samples from a randomized, controlled, crossover feeding trial, we evaluated the effects on metabolic profiles of a low-glycemic whole-grain dietary pattern (WG) compared with a dietary pattern high in refined grains and added sugars (RG) for 28 d. LC-MS-based targeted metabolomics analysis was performed on fasting plasma samples from 80 healthy participants (n = 40 men, n = 40 women) aged 18-45 y. Linear mixed models were used to evaluate differences in response between diets for individual metabolites. Kyoto Encyclopedia of Genes and Genomes (KEGG)-defined pathways and 2 novel data-driven analyses were conducted to consider differences at the pathway level.

resultsThere were 121 metabolites with detectable signal in >98% of all plasma samples. Eighteen metabolites were significantly different between diets at day 28 [false discovery rate (FDR) < 0.05]. Inositol, hydroxyphenylpyruvate, citrulline, ornithine, 13-hydroxyoctadecadienoic acid, glutamine, and oxaloacetate were higher after the WG diet than after the RG diet, whereas melatonin, betaine, creatine, acetylcholine, aspartate, hydroxyproline, methylhistidine, tryptophan, cystamine, carnitine, and trimethylamine were lower. Analyses using KEGG-defined pathways revealed statistically significant differences in tryptophan metabolism between diets, with kynurenine and melatonin positively associated with serum C-reactive protein concentrations. Novel data-driven methods at the metabolite and network levels found correlations among metabolites involved in branched-chain amino acid (BCAA) degradation, trimethylamine-N-oxide production, and β oxidation of fatty acids (FDR < 0.1) that differed between diets, with more favorable metabolic profiles detected after the WG diet. Higher BCAAs and trimethylamine were positively associated with homeostasis model assessment-insulin resistance.

conclusionsThese exploratory metabolomics results support beneficial effects of a low-glycemic load dietary pattern characterized by whole grains, legumes, fruits, and vegetables, compared with a diet high in refined grains and added sugars on inflammation and energy metabolism pathways. This trial was registered at clinicaltrials.gov as NCT00622661.

Indexed as

DietGlycemic LoadMetabolomicsAdolescentAdultBiomarkersEnergy MetabolismFeeding BehaviorFemaleHumansInflammationMaleMetabolomeYoung AdultBiomarkerscrossoverdietary interventiondietary patternsglycemic loadinflammationinsulin resistancemetabolomicswhole grains

Identifiers

PMID31432072
PMCPMC6766441
OpenAlexW2969797928

What OpenQuestion holds

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Registered trials

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.