↓ Skip to main content

Pretreatment gut microbiome predicts chemotherapy-related bloodstream infection

Overview of attention for article published in Genome Medicine, April 2016
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

news
5 news outlets
blogs
4 blogs
twitter
40 X users
patent
1 patent
facebook
1 Facebook page

Citations

dimensions_citation
142 Dimensions

Readers on

mendeley
220 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Pretreatment gut microbiome predicts chemotherapy-related bloodstream infection
Published in
Genome Medicine, April 2016
DOI 10.1186/s13073-016-0301-4
Pubmed ID
Authors

Emmanuel Montassier, Gabriel A. Al-Ghalith, Tonya Ward, Stephane Corvec, Thomas Gastinne, Gilles Potel, Philippe Moreau, Marie France de la Cochetiere, Eric Batard, Dan Knights

Abstract

Bacteremia, or bloodstream infection (BSI), is a leading cause of death among patients with certain types of cancer. A previous study reported that intestinal domination, defined as occupation of at least 30 % of the microbiota by a single bacterial taxon, is associated with BSI in patients undergoing allo-HSCT. However, the impact of the intestinal microbiome before treatment initiation on the risk of subsequent BSI remains unclear. Our objective was to characterize the fecal microbiome collected before treatment to identify microbes that predict the risk of BSI. We sampled 28 patients with non-Hodgkin lymphoma undergoing allogeneic hematopoietic stem cell transplantation (HSCT) prior to administration of chemotherapy and characterized 16S ribosomal RNA genes using high-throughput DNA sequencing. We quantified bacterial taxa and used techniques from machine learning to identify microbial biomarkers that predicted subsequent BSI. We found that patients who developed subsequent BSI exhibited decreased overall diversity and decreased abundance of taxa including Barnesiellaceae, Coriobacteriaceae, Faecalibacterium, Christensenella, Dehalobacterium, Desulfovibrio, and Sutterella. Using machine-learning methods, we developed a BSI risk index capable of predicting BSI incidence with a sensitivity of 90 % at a specificity of 90 % based only on the pretreatment fecal microbiome. These results suggest that the gut microbiota can identify high-risk patients before HSCT and that manipulation of the gut microbiota for prevention of BSI in high-risk patients may be a useful direction for future research. This approach may inspire the development of similar microbiome-based diagnostic and prognostic models in other diseases.

X Demographics

X Demographics

The data shown below were collected from the profiles of 40 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 220 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 2%
Brazil 1 <1%
Unknown 215 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 35 16%
Researcher 35 16%
Student > Master 29 13%
Student > Bachelor 20 9%
Other 19 9%
Other 39 18%
Unknown 43 20%
Readers by discipline Count As %
Medicine and Dentistry 57 26%
Agricultural and Biological Sciences 29 13%
Biochemistry, Genetics and Molecular Biology 26 12%
Immunology and Microbiology 15 7%
Computer Science 14 6%
Other 28 13%
Unknown 51 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 88. 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 10 December 2018.
All research outputs
#469,387
of 24,885,505 outputs
Outputs from Genome Medicine
#84
of 1,532 outputs
Outputs of similar age
#8,701
of 304,822 outputs
Outputs of similar age from Genome Medicine
#8
of 35 outputs
Altmetric has tracked 24,885,505 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,532 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.2. This one has done particularly well, scoring higher than 94% 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 304,822 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 97% of its contemporaries.
We're also able to compare this research output to 35 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.