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A coding and non-coding transcriptomic perspective on the genomics of human metabolic disease

Overview of attention for article published in Nucleic Acids Research, July 2018
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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 (96th percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

Mentioned by

news
6 news outlets
blogs
1 blog
twitter
31 X users
facebook
1 Facebook page
bluesky
1 Bluesky user

Readers on

mendeley
77 Mendeley
citeulike
1 CiteULike
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Article details
Title
A coding and non-coding transcriptomic perspective on the genomics of human metabolic disease
Published in
Nucleic Acids Research, July 2018
DOI 10.1093/nar/gky570
Pubmed ID
Authors
Abstract

Genome-wide association studies (GWAS), relying on hundreds of thousands of individuals, have revealed >200 genomic loci linked to metabolic disease (MD). Loss of insulin sensitivity (IS) is a key component of MD and we hypothesized that discovery of a robust IS transcriptome would help reveal the underlying genomic structure of MD. Using 1,012 human skeletal muscle samples, detailed physiology and a tissue-optimized approach for the quantification of coding (>18,000) and non-coding (>15,000) RNA (ncRNA), we identified 332 fasting IS-related genes (CORE-IS). Over 200 had a proven role in the biochemistry of insulin and/or metabolism or were located at GWAS MD loci. Over 50% of the CORE-IS genes responded to clinical treatment; 16 quantitatively tracking changes in IS across four independent studies (P = 0.0000053: negatively: AGL, G0S2, KPNA2, PGM2, RND3 and TSPAN9 and positively: ALDH6A1, DHTKD1, ECHDC3, MCCC1, OARD1, PCYT2, PRRX1, SGCG, SLC43A1 and SMIM8). A network of ncRNA positively related to IS and interacted with RNA coding for viral response proteins (P < 1 × 10-48), while reduced amino acid catabolic gene expression occurred without a change in expression of oxidative-phosphorylation genes. We illustrate that combining in-depth physiological phenotyping with robust RNA profiling methods, identifies molecular networks which are highly consistent with the genetics and biochemistry of human metabolic disease.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 31 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 77 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 77 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Researcher 12 16%
Student > Ph. D. Student 10 13%
Student > Bachelor 4 5%
Student > Master 4 5%
Other 3 4%
Other 13 17%
Unknown 31 40%
Readers by discipline
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 13 17%
Medicine and Dentistry 9 12%
Nursing and Health Professions 4 5%
Agricultural and Biological Sciences 3 4%
Computer Science 3 4%
Other 8 10%
Unknown 37 48%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 64. 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 25 June 2026.
All research outputs
#842,297
of 34,410,798 outputs
Outputs from Nucleic Acids Research
#313
of 33,919 outputs
Outputs of similar age
#13,579
of 370,989 outputs
Outputs of similar age from Nucleic Acids Research
#4
of 304 outputs
Altmetric has tracked 34,410,798 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 33,919 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.2. This one has done particularly well, scoring higher than 99% 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 370,989 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 96% of its contemporaries.
We're also able to compare this research output to 304 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 98% of its contemporaries.