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American College of Cardiology

Molecular Epidemiology of Heart Failure Translational Challenges and Opportunities

Overview of attention for article published in JACC: Basic to Translational Science, December 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (89th percentile)
  • Average Attention Score compared to outputs of the same age and source

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31 X users
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5 Facebook pages

Citations

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80 Mendeley
Title
Molecular Epidemiology of Heart Failure Translational Challenges and Opportunities
Published in
JACC: Basic to Translational Science, December 2017
DOI 10.1016/j.jacbts.2017.07.010
Pubmed ID
Authors

J. Gustav Smith

Abstract

Heart failure (HF) is the end-stage of all heart disease and arguably constitutes the greatest unmet therapeutic need in cardiovascular medicine today. Classic epidemiological studies have established clinical risk factors for HF, but the cause remains poorly understood in many cases. Biochemical analyses of small case-control series and animal models have described a plethora of molecular characteristics of HF, but a single unifying pathogenic theory is lacking. Heart failure appears to result not only from cardiac overload or injury but also from a complex interplay among genetic, neurohormonal, metabolic, inflammatory, and other biochemical factors acting on the heart. Recent development of robust, high-throughput tools in molecular biology provides opportunity for deep molecular characterization of population-representative cohorts and HF cases (molecular epidemiology), including genome sequencing, profiling of myocardial gene expression and chromatin modifications, plasma composition of proteins and metabolites, and microbiomes. The integration of such detailed information holds promise for improving understanding of HF pathophysiology in humans, identification of therapeutic targets, and definition of disease subgroups beyond the current classification based on ejection fraction which may benefit from improved individual tailoring of therapy. Challenges include: 1) the need for large cohorts with deep, uniform phenotyping; 2) access to the relevant tissues, ideally with repeated sampling to capture dynamic processes; and 3) analytical issues related to integration and analysis of complex datasets. International research consortia have formed to address these challenges and combine datasets, and cohorts with up to 1 million participants are being collected. This paper describes the molecular epidemiology of HF and provides an overview of methods and tissue types and examples of published and ongoing efforts to systematically evaluate molecular determinants of HF in human populations.

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 readers

Mendeley readers

The data shown below were compiled from readership statistics for 80 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 80 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 14%
Student > Master 9 11%
Student > Bachelor 8 10%
Researcher 7 9%
Other 5 6%
Other 16 20%
Unknown 24 30%
Readers by discipline Count As %
Medicine and Dentistry 21 26%
Biochemistry, Genetics and Molecular Biology 11 14%
Nursing and Health Professions 6 8%
Agricultural and Biological Sciences 3 4%
Chemistry 2 3%
Other 4 5%
Unknown 33 41%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 18. 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 01 August 2018.
All research outputs
#2,078,022
of 25,746,891 outputs
Outputs from JACC: Basic to Translational Science
#181
of 822 outputs
Outputs of similar age
#45,601
of 451,399 outputs
Outputs of similar age from JACC: Basic to Translational Science
#8
of 14 outputs
Altmetric has tracked 25,746,891 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 822 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.0. This one has done well, scoring higher than 77% 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 451,399 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 89% of its contemporaries.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.