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Phenotypic Heterogeneity in Dementia: A Challenge for Epidemiology and Biomarker Studies

Overview of attention for article published in Frontiers in Public Health, June 2018
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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 (81st percentile)
  • Good Attention Score compared to outputs of the same age and source (70th percentile)

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1 news outlet
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3 X users

Citations

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57 Dimensions

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77 Mendeley
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Title
Phenotypic Heterogeneity in Dementia: A Challenge for Epidemiology and Biomarker Studies
Published in
Frontiers in Public Health, June 2018
DOI 10.3389/fpubh.2018.00181
Pubmed ID
Authors

Joanne Ryan, Peter Fransquet, Jo Wrigglesworth, Paul Lacaze

Abstract

Dementia can result from a number of distinct diseases with differing etiology and pathophysiology. Even within the same disease, there is considerable phenotypic heterogeneity with varying symptoms and disease trajectories. Dementia diagnosis is thus very complex, time-consuming, and expensive and can only be made definitively post-mortem with histopathological confirmation. These inherent difficulties combined with the overlap of some symptoms and even neuropathological features, present a challenging problem for research in the field. This has likely hampered progress in epidemiological studies of risk factors and preventative interventions, as well as genetic and biomarker research. Resource limitations in large epidemiologically studies mean that limited diagnostic criteria are often used, which can result in phenotypically heterogeneous disease states being grouped together, potentially resulting in misclassification bias. When biomarkers are identified for etiologically heterogeneous diseases, they will have low specificity for any utility in clinical practice, even if their sensitivity is high. We highlight several challenges in in the field which must be addressed for the success of future genetic and biomarker studies, and may be key to the development of the most effective treatments. As a step toward achieving this goal, defining the dementia as a biological construct based on the presence of specific pathological features, rather than clinical symptoms, will enable more precise predictive models. It has the potential to lead to the discovery of novel genetic variants, as well as the identification of individuals at heightened risk of the disease, even prior to the appearance of clinical symptoms.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 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 77 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 77 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 17%
Researcher 13 17%
Student > Master 12 16%
Student > Bachelor 3 4%
Unspecified 3 4%
Other 12 16%
Unknown 21 27%
Readers by discipline Count As %
Medicine and Dentistry 12 16%
Neuroscience 9 12%
Biochemistry, Genetics and Molecular Biology 7 9%
Nursing and Health Professions 4 5%
Unspecified 4 5%
Other 15 19%
Unknown 26 34%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 December 2019.
All research outputs
#2,923,424
of 23,090,520 outputs
Outputs from Frontiers in Public Health
#1,102
of 10,396 outputs
Outputs of similar age
#61,467
of 328,030 outputs
Outputs of similar age from Frontiers in Public Health
#25
of 88 outputs
Altmetric has tracked 23,090,520 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 10,396 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 10.0. This one has done well, scoring higher than 89% 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 328,030 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 81% of its contemporaries.
We're also able to compare this research output to 88 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 70% of its contemporaries.