Evidence map›Paper›PMID 37125033›Full record

ArticleFrontiers in nutrition2023

A discriminant analysis of plasma metabolomics for the assessment of metabolic responsiveness to red raspberry consumption.

Valentin Barbe, Juan de Toro-Martín, Rodrigo San-Cristobal, Véronique Garneau, Geneviève Pilon, Patrick Couture, Denis Roy, Charles Couillard, André Marette, Marie-Claude Vohl

Open access · goldAbstract read
In one paragraph

Article in Frontiers in nutrition, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.5field-weighted citation impact, top 17% 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

1 citing paper in PubMed, 7 citations in OpenAlex.

  1. Trial
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

10 authors at 2 institutions in 1 country.

Valentin BarbeCentre Nutrition, santé et société (NUTRISS), Université Laval, Québec City, QC, Canada.
Juan de Toro-MartínCentre Nutrition, santé et société (NUTRISS), Université Laval, Québec City, QC, Canada.
Rodrigo San-CristobalCentre Nutrition, santé et société (NUTRISS), Université Laval, Québec City, QC, Canada.
Véronique GarneauCentre Nutrition, santé et société (NUTRISS), Université Laval, Québec City, QC, Canada.
Geneviève PilonCentre Nutrition, santé et société (NUTRISS), Université Laval, Québec City, QC, Canada.
Patrick CoutureCentre Nutrition, santé et société (NUTRISS), Université Laval, Québec City, QC, Canada.
Denis RoyInstitut sur la nutrition et les aliments fonctionnels (INAF), Université Laval, Québec City, QC, Canada.
Charles CouillardCentre Nutrition, santé et société (NUTRISS), Université Laval, Québec City, QC, Canada.
André MaretteCentre Nutrition, santé et société (NUTRISS), Université Laval, Québec City, QC, Canada.
Marie-Claude VohlCentre Nutrition, santé et société (NUTRISS), Université Laval, Québec City, QC, Canada.
Université Laval · CAMontreal Heart Institute · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Many studies show that the intake of raspberries is beneficial to immune-metabolic health, but the responses of individuals are heterogeneous and not fully understood. Methods: In a two-arm parallel-group, randomized, controlled trial, immune-metabolic outcomes and plasma metabolite levels were analyzed before and after an 8-week red raspberry consumption. Based on partial least squares discriminant analysis (PLS-DA) on plasma xenobiotic levels, adherence to the intervention was first evaluated. A second PLS-DA followed by hierarchical clustering was used to classify individuals into response subgroups. Clinical immune and metabolic outcomes, including insulin resistance (HOMA-IR) and sensitivity (Matsuda, QUICKI) indices, during the intervention were assessed and compared between response subgroups. Results: Two subgroups of participants, type 1 responders ( Conclusion: The beneficial effects associated with red raspberry consumption are subject to inter-individual variability. Metabolomics-based clustering appears to be an effective way to assess adherence to a nutritional intervention and to classify individuals according to their immune-metabolic responsiveness to the intervention. This approach may be replicated in future studies to provide a better understanding of how interindividual variability impacts the effects of nutritional interventions on immune-metabolic health.

Indexed as

clusteringmachine learningmetabolic healthmetabolomicsprecision nutritionraspberry

Identifiers

PMID37125033
PMCPMC10130762
OpenAlexW4360617747

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.