| Title |
Pharmacogenetics in type 2 diabetes: influence on response to oral hypoglycemic agents
|
|---|---|
| Published in |
Pharmacogenomics and Personalized Medicine, April 2016
|
| DOI | 10.2147/pgpm.s84854 |
| Pubmed ID | |
| Authors |
Adem Yesuf Dawed, Kaixin Zhou, Ewan Robert Pearson |
| Abstract |
Type 2 diabetes is one of the leading causes of morbidity and mortality, consuming a significant proportion of public health spending. Oral hypoglycemic agents (OHAs) are the frontline treatment approaches after lifestyle changes. However, huge interindividual variation in response to OHAs results in unnecessary treatment failure. In addition to nongenetic factors, genetic factors are thought to contribute to much of such variability, highlighting the importance of the potential of pharmacogenetics to improve therapeutic outcome. Despite the presence of conflicting results, significant progress has been made in an effort to identify the genetic markers associated with pharmacokinetics, pharmacodynamics, and ultimately therapeutic response and/or adverse outcomes to OHAs. As such, this article presents a comprehensive review of current knowledge on pharmacogenetics of OHAs and provides insights into knowledge gaps and future directions. |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 40% |
| Unknown | 3 | 60% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 60% |
| Practitioners (doctors, other healthcare professionals) | 2 | 40% |
Mendeley readers
The data shown below were compiled from readership statistics for 74 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 74 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 12 | 16% |
| Student > Bachelor | 11 | 15% |
| Researcher | 10 | 14% |
| Student > Ph. D. Student | 7 | 9% |
| Other | 5 | 7% |
| Other | 10 | 14% |
| Unknown | 19 | 26% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 15 | 20% |
| Medicine and Dentistry | 13 | 18% |
| Pharmacology, Toxicology and Pharmaceutical Science | 10 | 14% |
| Agricultural and Biological Sciences | 5 | 7% |
| Nursing and Health Professions | 2 | 3% |
| Other | 8 | 11% |
| Unknown | 21 | 28% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. 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 29 April 2019.
All research outputs
#5,269,238
of 25,748,735 outputs
Outputs from Pharmacogenomics and Personalized Medicine
#1
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Outputs of similar age
#76,583
of 315,638 outputs
Outputs of similar age from Pharmacogenomics and Personalized Medicine
#1
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Altmetric has tracked 25,748,735 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.8. This one scored the same or higher as 0 of them.
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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