Evidence map›Paper›PMID 36005608›Full record

ArticleMetabolites2022

K-Clique Multiomics Framework: A Novel Protocol to Decipher the Role of Gut Microbiota Communities in Nutritional Intervention Trials.

Carlo Mengucci, Lorenzo Nissen, Gianfranco Picone, Corinne Malpuech-Brugère, Caroline Orfila, Luigi Ricciardiello, Alessandra Bordoni, Francesco Capozzi, Andrea Gianotti

Abstract read
In one paragraph

Article in Metabolites, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Biological Magnetic Resonance Data Bank.Nucleic acids research · 2023
    Article
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

9 authors.

Carlo MengucciDepartment of Agricultural and Food Sciences (DISTAL), University of Bologna, 47521 Cesena, Italy.ORCID 0000-0003-0602-3003
Lorenzo NissenDepartment of Agricultural and Food Sciences (DISTAL), University of Bologna, 47521 Cesena, Italy.ORCID 0000-0002-7875-6321
Gianfranco PiconeDepartment of Agricultural and Food Sciences (DISTAL), University of Bologna, 47521 Cesena, Italy.ORCID 0000-0001-7932-6692
Corinne Malpuech-BrugèreUnité de Nutrition Humaine (UNH), Université Clermont Auvergne, INRAE, CRNH Auvergne, F-63000 Clermont-Ferrand, France.ORCID 0000-0001-9286-4647
Caroline OrfilaSchool of Food Science and Nutrition, University of Leeds, Leeds LS2 9JT, UK.ORCID 0000-0003-2564-8068
Luigi RicciardielloGastroenterological Unit, Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40138 Bologna, Italy.ORCID 0000-0003-2568-6208
Alessandra BordoniDepartment of Agricultural and Food Sciences (DISTAL), University of Bologna, 47521 Cesena, Italy.ORCID 0000-0003-4579-1662
Francesco CapozziDepartment of Agricultural and Food Sciences (DISTAL), University of Bologna, 47521 Cesena, Italy.ORCID 0000-0002-5543-2359
Andrea GianottiDepartment of Agricultural and Food Sciences (DISTAL), University of Bologna, 47521 Cesena, Italy.ORCID 0000-0002-8847-6251

Funding

European Union FP7 grant agreement n° 311876: Pathway-27.
6 · The paper itself

Abstract

The availability of omics data providing information from different layers of complex biological processes that link nutrition to human health would benefit from the development of integrated approaches combining holistically individual omics data, including those associated with the microbiota that impacts the metabolisation and bioavailability of food components. Microbiota must be considered as a set of populations of interconnected consortia, with compensatory capacities to adapt to different nutritional intake. To study the consortium nature of the microbiome, we must rely on specially designed data analysis tools. The purpose of this work is to propose the construction of a general correlation network-based explorative tool, suitable for nutritional clinical trials, by integrating omics data from faecal microbial taxa, stool metabolome (1H NMR spectra) and GC-MS for stool volatilome. The presented approach exploits a descriptive paradigm necessary for a true multiomics integration of data, which is a powerful tool to investigate the complex physiological effects of nutritional interventions.

Indexed as

faecal metabolomicsk-clique communitiesmachine learningmetagenomicsnetwork of interactionsvolatilome

Identifiers

PMID36005608
PMCPMC9412844

What OpenQuestion holds

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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.