Evidence map›Paper›PMID 31105659›Full record

ArticleFrontiers in microbiology2019

Adaptation of the Human Gut Microbiota Metabolic Network During the First Year After Birth.

Alvaro Fuertes, Sergio Pérez-Burillo, Iñigo Apaolaza, Yvonne Vallès, M Pilar Francino, José Ángel Rufián-Henares, Francisco J Planes

Open access · goldAbstract read
In one paragraph

Article in Frontiers in microbiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
1.4field-weighted citation impact, top 19% 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

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Review
  6. Review
  7. Article
  8. Article
  9. Article
  10. Review
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

7 authors at 4 institutions in 2 countries.

Alvaro FuertesBioinformatics Group, TECNUN School of Engineering, Department of Biomedical Engineering and Sciences, Universidad de Navarra, San Sebastián, Spain.
Sergio Pérez-BurilloDepartamento de Nutrición y Bromatología, Instituto de Nutrición y Tecnología de los Alimentos, Centro de Investigación Biomédica, Universidad de Granada, Granada, Spain.
Iñigo ApaolazaBioinformatics Group, TECNUN School of Engineering, Department of Biomedical Engineering and Sciences, Universidad de Navarra, San Sebastián, Spain.
Yvonne VallèsUnitat Mixta d'Investigació en Genòmica i Salut, Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana-Salud Pública/Instituto de Biología Integrativa de Sistemas, Universitat de València, Valencia, Spain.
M Pilar FrancinoUnitat Mixta d'Investigació en Genòmica i Salut, Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana-Salud Pública/Instituto de Biología Integrativa de Sistemas, Universitat de València, Valencia, Spain.
José Ángel Rufián-HenaresDepartamento de Nutrición y Bromatología, Instituto de Nutrición y Tecnología de los Alimentos, Centro de Investigación Biomédica, Universidad de Granada, Granada, Spain.
Francisco J PlanesBioinformatics Group, TECNUN School of Engineering, Department of Biomedical Engineering and Sciences, Universidad de Navarra, San Sebastián, Spain.
Centro de Estudios e Investigaciones Técnicas de Gipuzkoa · ESUniversidad de Granada · ESFundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana · ESUniversitat de València · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Predicting the metabolic behavior of the human gut microbiota in different contexts is one of the most promising areas of constraint-based modeling. Recently, we presented a supra-organismal approach to build context-specific metabolic networks of bacterial communities using functional and taxonomic assignments of meta-omics data. In this work, this algorithm is applied to elucidate the metabolic changes induced over the first year after birth in the gut microbiota of a cohort of Spanish infants. We used metagenomics data of fecal samples and nutritional data of 13 infants at five time points. The resulting networks for each time point were analyzed, finding significant alterations once solid food is introduced in the diet. Our work shows that solid food leads to a different pattern of output metabolites that can be potentially released from the gut microbiota to the host. Experimental validation is presented for ferulate, a neuroprotective metabolite involved in the gut-brain axis.

Indexed as

human gut microbiomemetabolic networksmetabolomicsmetagenomicspersonalized nutrition

Identifiers

PMID31105659
PMCPMC6491881
OpenAlexW2941179678

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.