| Title |
Prevalence of clinically actionable genotypes and medication exposure of older adults in the community
|
|---|---|
| Published in |
Pharmacogenomics and Personalized Medicine, January 2017
|
| DOI | 10.2147/pgpm.s123719 |
| Pubmed ID | |
| Authors |
Nilofar Daneshi, Elizabeth Holliday, Stephen Hancock, Jennifer J Schneider, Rodney J Scott, John Attia, Elizabeth A Milward |
| Abstract |
This study analyzed clinically actionable pharmacogenotypes for clopidogrel, warfarin, statins, thiopurines, and tacrolimus using microarray data for 2121 participants (55-85 years) from the Australian Hunter Community Study (HCS). At least 74% of participants (95% confidence interval [CI]: 72%-76%) had strong level evidence for at least one medium- or high-risk actionable genotype that would trigger a change in standard therapy under current international recommendations. About 14% of these participants (95% CI: 12%-16%) were taking medication potentially affected by the genotype in question. Furthermore, ~2.6% of all participants with medication data (95% CI: 1.4%-3.8%) had a high-risk clinically actionable genotype for a medication to which they were exposed. This represents a considerable number of people at the population level. Although relationships between genotype and health outcomes remain contentious, pharmacogenotyping of multiple variants simultaneously may have considerable potential to improve medication safety and efficacy for older people in the community. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 38 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Other | 4 | 11% |
| Student > Ph. D. Student | 3 | 8% |
| Researcher | 3 | 8% |
| Student > Bachelor | 2 | 5% |
| Student > Doctoral Student | 2 | 5% |
| Other | 6 | 16% |
| Unknown | 18 | 47% |
| Readers by discipline | Count | As % |
|---|---|---|
| Pharmacology, Toxicology and Pharmaceutical Science | 7 | 18% |
| Biochemistry, Genetics and Molecular Biology | 6 | 16% |
| Medicine and Dentistry | 4 | 11% |
| Agricultural and Biological Sciences | 1 | 3% |
| Computer Science | 1 | 3% |
| Other | 1 | 3% |
| Unknown | 18 | 47% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 31 January 2017.
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#20,247,404
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#1
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Outputs of similar age from Pharmacogenomics and Personalized Medicine
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