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Altered gut microbiota in female mice with persistent low body weights following removal of post-weaning chronic dietary restriction

Overview of attention for article published in Genome Medicine, October 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (78th percentile)
  • Average Attention Score compared to outputs of the same age and source

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1 blog
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Citations

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22 Dimensions

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143 Mendeley
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2 CiteULike
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Title
Altered gut microbiota in female mice with persistent low body weights following removal of post-weaning chronic dietary restriction
Published in
Genome Medicine, October 2016
DOI 10.1186/s13073-016-0357-1
Pubmed ID
Authors

Jun Chen, Yoshitaka Toyomasu, Yujiro Hayashi, David R. Linden, Joseph H. Szurszewski, Heidi Nelson, Gianrico Farrugia, Purna C. Kashyap, Nicholas Chia, Tamas Ordog

Abstract

Nutritional interventions often fail to prevent growth failure in childhood and adolescent malnutrition and the mechanisms remain unclear. Recent studies revealed altered microbiota in malnourished children and anorexia nervosa. To facilitate mechanistic studies under physiologically relevant conditions, we established a mouse model of growth failure following chronic dietary restriction and examined microbiota in relation to age, diet, body weight, and anabolic treatment. Four-week-old female BALB/c mice (n = 12/group) were fed ad libitum (AL) or offered limited food to abolish weight gain (LF). A subset of restricted mice was treated with an insulin-like growth factor 1 (IGF1) analog. Food access was restored in a subset of untreated LF (LF-RF) and IGF1-treated LF mice (TLF-RF) on day 97. Gut microbiota were determined on days 69, 96-99 and 120 by next generation sequencing of the V3-5 region of the 16S rRNA gene. Microbiota-host factor associations were analyzed by distance-based PERMANOVA and quantified by the coefficient of determination R(2) for age, diet, and normalized body weight change (Δbwt). Microbial taxa on day 120 were compared following fitting with an overdispersed Poisson regression model. The machine learning algorithm Random Forests was used to predict age based on the microbiota. On day 120, Δbwt in AL, LF, LF-RF, and TLF-RF mice was 52 ± 3, -6 ± 1*, 40 ± 3*, and 46 ± 2 % (*, P < 0.05 versus AL). Age and diet, but not Δbwt, were associated with gut microbiota composition. Age explained a larger proportion of the microbiota variability than diet or Δbwt. Random Forests predicted chronological age based on the microbiota and indicated microbiota immaturity in the LF mice before, but not after, refeeding. However, on day 120, the microbiota community structure of LF-RF mice was significantly different from that of both AL and LF mice. IGF1 mitigated the difference from the AL group. Refed groups had a higher abundance of Bacteroidetes and Proteobacteria and a lower abundance of Firmicutes than AL mice. Persistent growth failure can be induced by 97-day dietary restriction in young female mice and is associated with microbiota changes seen in lean mice and individuals and anorexia nervosa. IGF1 facilitates recovery of body weights and microbiota.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 143 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 143 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 21 15%
Researcher 20 14%
Student > Master 19 13%
Student > Doctoral Student 10 7%
Student > Ph. D. Student 9 6%
Other 28 20%
Unknown 36 25%
Readers by discipline Count As %
Medicine and Dentistry 24 17%
Biochemistry, Genetics and Molecular Biology 17 12%
Agricultural and Biological Sciences 13 9%
Nursing and Health Professions 10 7%
Neuroscience 10 7%
Other 20 14%
Unknown 49 34%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 12 October 2016.
All research outputs
#4,090,436
of 22,890,496 outputs
Outputs from Genome Medicine
#828
of 1,443 outputs
Outputs of similar age
#68,691
of 321,456 outputs
Outputs of similar age from Genome Medicine
#19
of 32 outputs
Altmetric has tracked 22,890,496 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,443 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 25.8. This one is in the 42nd percentile – i.e., 42% of its peers scored the same or lower than it.
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 321,456 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 78% of its contemporaries.
We're also able to compare this research output to 32 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.