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Novel Developmental Analyses Identify Longitudinal Patterns of Early Gut Microbiota that Affect Infant Growth

Overview of attention for article published in PLoS Computational Biology, May 2013
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  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (95th percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

Mentioned by

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4 news outlets
twitter
9 X users
facebook
2 Facebook pages
googleplus
1 Google+ user

Citations

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75 Dimensions

Readers on

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193 Mendeley
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Title
Novel Developmental Analyses Identify Longitudinal Patterns of Early Gut Microbiota that Affect Infant Growth
Published in
PLoS Computational Biology, May 2013
DOI 10.1371/journal.pcbi.1003042
Pubmed ID
Authors

Richard A. White, Jørgen V. Bjørnholt, Donna D. Baird, Tore Midtvedt, Jennifer R. Harris, Marcello Pagano, Winston Hide, Knut Rudi, Birgitte Moen, Nina Iszatt, Shyamal D. Peddada, Merete Eggesbø

Abstract

It is acknowledged that some obesity trajectories are set early in life, and that rapid weight gain in infancy is a risk factor for later development of obesity. Identifying modifiable factors associated with early rapid weight gain is a prerequisite for curtailing the growing worldwide obesity epidemic. Recently, much attention has been given to findings indicating that gut microbiota may play a role in obesity development. We aim at identifying how the development of early gut microbiota is associated with expected infant growth. We developed a novel procedure that allows for the identification of longitudinal gut microbiota patterns (corresponding to the gut ecosystem developing), which are associated with an outcome of interest, while appropriately controlling for the false discovery rate. Our method identified developmental pathways of Staphylococcus species and Escherichia coli that were associated with expected growth, and traditional methods indicated that the detection of Bacteroides species at day 30 was associated with growth. Our method should have wide future applicability for studying gut microbiota, and is particularly important for translational considerations, as it is critical to understand the timing of microbiome transitions prior to attempting to manipulate gut microbiota in early life.

X Demographics

X Demographics

The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 193 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 1%
United States 2 1%
Denmark 2 1%
Sweden 1 <1%
Switzerland 1 <1%
Canada 1 <1%
Japan 1 <1%
Russia 1 <1%
Unknown 182 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 46 24%
Student > Ph. D. Student 34 18%
Student > Master 23 12%
Student > Bachelor 17 9%
Other 11 6%
Other 34 18%
Unknown 28 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 56 29%
Medicine and Dentistry 38 20%
Biochemistry, Genetics and Molecular Biology 21 11%
Immunology and Microbiology 10 5%
Nursing and Health Professions 8 4%
Other 26 13%
Unknown 34 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 37. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 September 2019.
All research outputs
#1,101,939
of 25,711,518 outputs
Outputs from PLoS Computational Biology
#871
of 9,024 outputs
Outputs of similar age
#8,324
of 206,257 outputs
Outputs of similar age from PLoS Computational Biology
#8
of 115 outputs
Altmetric has tracked 25,711,518 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,024 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.3. This one has done particularly well, scoring higher than 90% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 206,257 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% of its contemporaries.
We're also able to compare this research output to 115 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 93% of its contemporaries.