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Characterization of oral and gut microbiome temporal variability in hospitalized cancer patients

Overview of attention for article published in Genome Medicine, February 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (88th percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

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1 blog
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22 X users

Citations

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77 Dimensions

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192 Mendeley
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Title
Characterization of oral and gut microbiome temporal variability in hospitalized cancer patients
Published in
Genome Medicine, February 2017
DOI 10.1186/s13073-017-0409-1
Pubmed ID
Authors

Jessica R. Galloway-Peña, Daniel P. Smith, Pranoti Sahasrabhojane, W. Duncan Wadsworth, Bryan M. Fellman, Nadim J. Ajami, Elizabeth J. Shpall, Naval Daver, Michele Guindani, Joseph F. Petrosino, Dimitrios P. Kontoyiannis, Samuel A. Shelburne

Abstract

Understanding longitudinal variability of the microbiome in ill patients is critical to moving microbiome-based measurements and therapeutics into clinical practice. However, the vast majority of data regarding microbiome stability are derived from healthy subjects. Herein, we sought to determine intra-patient temporal microbiota variability, the factors driving such variability, and its clinical impact in an extensive longitudinal cohort of hospitalized cancer patients during chemotherapy. The stool (n = 365) and oral (n = 483) samples of 59 patients with acute myeloid leukemia (AML) undergoing induction chemotherapy (IC) were sampled from initiation of chemotherapy until neutrophil recovery. Microbiome characterization was performed via analysis of 16S rRNA gene sequencing. Temporal variability was determined using coefficients of variation (CV) of the Shannon diversity index (SDI) and unweighted and weighted UniFrac distances per patient, per site. Measurements of intra-patient temporal variability and patient stability categories were analyzed for their correlations with genera abundances. Groups of patients were analyzed to determine if patients with adverse outcomes had significantly different levels of microbiome temporal variability. Potential clinical drivers of microbiome temporal instability were determined using multivariable regression analyses. Our cohort evidenced a high degree of intra-patient temporal instability of stool and oral microbial diversity based on SDI CV. We identified statistically significant differences in the relative abundance of multiple taxa amongst individuals with different levels of microbiota temporal stability. Increased intra-patient temporal variability of the oral SDI was correlated with increased risk of infection during IC (P = 0.02), and higher stool SDI CVs were correlated with increased risk of infection 90 days post-IC (P = 0.04). Total days on antibiotics was significantly associated with increased temporal variability of both oral microbial diversity (P = 0.03) and community structure (P = 0.002). These data quantify the longitudinal variability of the oral and gut microbiota in AML patients, show that increased variability was correlated with adverse clinical outcomes, and offer the possibility of using stabilizing taxa as a method of focused microbiome repletion. Furthermore, these results support the importance of longitudinal microbiome sampling and analyses, rather than one time measurements, in research and future clinical practice.

X Demographics

X Demographics

The data shown below were collected from the profiles of 22 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 192 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 <1%
United States 1 <1%
Unknown 190 99%

Demographic breakdown

Readers by professional status Count As %
Student > Master 27 14%
Researcher 24 13%
Student > Ph. D. Student 23 12%
Student > Postgraduate 12 6%
Student > Bachelor 12 6%
Other 45 23%
Unknown 49 26%
Readers by discipline Count As %
Agricultural and Biological Sciences 34 18%
Medicine and Dentistry 32 17%
Biochemistry, Genetics and Molecular Biology 22 11%
Immunology and Microbiology 12 6%
Computer Science 5 3%
Other 24 13%
Unknown 63 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 19. 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 11 August 2017.
All research outputs
#1,862,098
of 24,885,505 outputs
Outputs from Genome Medicine
#413
of 1,532 outputs
Outputs of similar age
#35,634
of 316,221 outputs
Outputs of similar age from Genome Medicine
#11
of 30 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 92nd percentile: it's in the top 10% 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 gotten more attention than average, scoring higher than 73% 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 316,221 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 88% of its contemporaries.
We're also able to compare this research output to 30 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.