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Beyond genomics: understanding exposotypes through metabolomics

Overview of attention for article published in Human Genomics, January 2018
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#5 of 394)
  • High Attention Score compared to outputs of the same age (95th percentile)

Mentioned by

1 blog
87 tweeters
1 Facebook page


46 Dimensions

Readers on

148 Mendeley
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Beyond genomics: understanding exposotypes through metabolomics
Published in
Human Genomics, January 2018
DOI 10.1186/s40246-018-0134-x
Pubmed ID

Nicholas J. W. Rattray, Nicole C. Deziel, Joshua D. Wallach, Sajid A. Khan, Vasilis Vasiliou, John P. A. Ioannidis, Caroline H. Johnson


Over the past 20 years, advances in genomic technology have enabled unparalleled access to the information contained within the human genome. However, the multiple genetic variants associated with various diseases typically account for only a small fraction of the disease risk. This may be due to the multifactorial nature of disease mechanisms, the strong impact of the environment, and the complexity of gene-environment interactions. Metabolomics is the quantification of small molecules produced by metabolic processes within a biological sample. Metabolomics datasets contain a wealth of information that reflect the disease state and are consequent to both genetic variation and environment. Thus, metabolomics is being widely adopted for epidemiologic research to identify disease risk traits. In this review, we discuss the evolution and challenges of metabolomics in epidemiologic research, particularly for assessing environmental exposures and providing insights into gene-environment interactions, and mechanism of biological impact. Metabolomics can be used to measure the complex global modulating effect that an exposure event has on an individual phenotype. Combining information derived from all levels of protein synthesis and subsequent enzymatic action on metabolite production can reveal the individual exposotype. We discuss some of the methodological and statistical challenges in dealing with this type of high-dimensional data, such as the impact of study design, analytical biases, and biological variance. We show examples of disease risk inference from metabolic traits using metabolome-wide association studies. We also evaluate how these studies may drive precision medicine approaches, and pharmacogenomics, which have up to now been inefficient. Finally, we discuss how to promote transparency and open science to improve reproducibility and credibility in metabolomics. Comparison of exposotypes at the human population level may help understanding how environmental exposures affect biology at the systems level to determine cause, effect, and susceptibilities. Juxtaposition and integration of genomics and metabolomics information may offer additional insights. Clinical utility of this information for single individuals and populations has yet to be routinely demonstrated, but hopefully, recent advances to improve the robustness of large-scale metabolomics will facilitate clinical translation.

Twitter Demographics

The data shown below were collected from the profiles of 87 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 148 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 148 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 31 21%
Researcher 28 19%
Student > Bachelor 14 9%
Student > Master 13 9%
Professor 7 5%
Other 18 12%
Unknown 37 25%
Readers by discipline Count As %
Agricultural and Biological Sciences 27 18%
Biochemistry, Genetics and Molecular Biology 23 16%
Medicine and Dentistry 15 10%
Chemistry 8 5%
Environmental Science 7 5%
Other 23 16%
Unknown 45 30%

Attention Score in Context

This research output has an Altmetric Attention Score of 55. 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 15 December 2018.
All research outputs
of 19,659,155 outputs
Outputs from Human Genomics
of 394 outputs
Outputs of similar age
of 387,991 outputs
Outputs of similar age from Human Genomics
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Altmetric has tracked 19,659,155 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 394 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.5. This one has done particularly well, scoring higher than 98% 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 387,991 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 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them