| Title |
Proteomics for prediction of disease progression and response to therapy in diabetic kidney disease
|
|---|---|
| Published in |
Diabetologia, June 2016
|
| DOI | 10.1007/s00125-016-4001-9 |
| Pubmed ID | |
| Authors |
Michelle J. Pena, Harald Mischak, Hiddo J. L. Heerspink |
| Abstract |
The past decade has resulted in multiple new findings of potential proteomic biomarkers of diabetic kidney disease (DKD). Many of these biomarkers reflect an important role in the (patho)physiology and biological processes of DKD. Situations in which proteomics could be applied in clinical practice include the identification of individuals at risk of progressive kidney disease and those who would respond well to treatment, in order to tailor therapy for those at highest risk. However, while many proteomic biomarkers have been discovered, and even found to be predictive, most lack rigorous external validation in sufficiently powered studies with renal endpoints. Moreover, studies assessing short-term changes in the proteome for therapy-monitoring purposes are lacking. Collaborations between academia and industry and enhanced interactions with regulatory agencies are needed to design new, sufficiently powered studies to implement proteomics in clinical practice. |
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 % |
|---|---|---|
| Spain | 1 | 20% |
| Canada | 1 | 20% |
| United Kingdom | 1 | 20% |
| United States | 1 | 20% |
| Unknown | 1 | 20% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 60% |
| Scientists | 2 | 40% |
Mendeley readers
The data shown below were compiled from readership statistics for 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 1% |
| Unknown | 72 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 11 | 15% |
| Student > Bachelor | 11 | 15% |
| Researcher | 10 | 14% |
| Other | 7 | 10% |
| Student > Master | 6 | 8% |
| Other | 14 | 19% |
| Unknown | 14 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 19 | 26% |
| Biochemistry, Genetics and Molecular Biology | 11 | 15% |
| Agricultural and Biological Sciences | 6 | 8% |
| Engineering | 3 | 4% |
| Chemistry | 3 | 4% |
| Other | 12 | 16% |
| Unknown | 19 | 26% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 26 August 2016.
All research outputs
#13,240,131
of 22,880,230 outputs
Outputs from Diabetologia
#4,178
of 5,038 outputs
Outputs of similar age
#182,118
of 352,154 outputs
Outputs of similar age from Diabetologia
#61
of 85 outputs
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