Proteomics for prediction of disease progression and response to therapy in diabetic kidney disease
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...
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| Veröffentlicht in: | Diabetologia 2016-09, Vol.59 (9), p.1819-1831 |
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| container_title | Diabetologia |
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| creator | Pena, Michelle J. Mischak, Harald Heerspink, Hiddo J. L. |
| description | 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. |
| doi_str_mv | 10.1007/s00125-016-4001-9 |
| format | Article |
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L.</creatorcontrib><title>Proteomics for prediction of disease progression and response to therapy in diabetic kidney disease</title><title>Diabetologia</title><addtitle>Diabetologia</addtitle><addtitle>Diabetologia</addtitle><description>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.</description><subject>Animals</subject><subject>Biomarkers - metabolism</subject><subject>Diabetes Mellitus - metabolism</subject><subject>Diabetes Mellitus - pathology</subject><subject>Diabetic Nephropathies - metabolism</subject><subject>Diabetic Nephropathies - pathology</subject><subject>Disease Progression</subject><subject>Human Physiology</subject><subject>Humans</subject><subject>Internal Medicine</subject><subject>Kidney Diseases - metabolism</subject><subject>Kidney Diseases - pathology</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Metabolic Diseases</subject><subject>Proteome - metabolism</subject><subject>Proteomics - methods</subject><subject>Review</subject><issn>0012-186X</issn><issn>1432-0428</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><recordid>eNqNkcFrFDEUxoNY7Fr9A7zIgBcvY18mmUxyEaRoFQr1oOAtZJKXbepuMiazwv73ZrptqULBUx7v-70vefkIeUXhHQUYTgsA7foWqGh5LVv1hKwoZ10LvJNPyWqRWyrFj2PyvJRrAGA9F8_IcTcwzhmFFbFfc5oxbYMtjU-5mTK6YOeQYpN840JBU7B20zpjKUvbRNfUekqxCnNq5ivMZto3IVbcjDgH2_wMLuL-bvwFOfJmU_Dl7XlCvn_6-O3sc3txef7l7MNFa3uh5taP4Bn1RvXGSOnRKa8cZ0wNQ0d7aqDvLbec2wFd74UaHYx1OcdgZEpIyk7I-4PvtBu36CzGOZuNnnLYmrzXyQT9txLDlV6n35oroRhbDN7eGuT0a4dl1ttQLG42JmLaFU0lpVJSIdV_oCAFiKFf0Df_oNdpl2P9iRvDmkknWKXogbI5lZLR37-bgl7S1oe0dU1bL2nrxfn1w4XvJ-7irUB3AEqV4hrzg6sfdf0DUMy2Qw</recordid><startdate>20160901</startdate><enddate>20160901</enddate><creator>Pena, Michelle J.</creator><creator>Mischak, Harald</creator><creator>Heerspink, Hiddo J. 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| subjects | Animals Biomarkers - metabolism Diabetes Mellitus - metabolism Diabetes Mellitus - pathology Diabetic Nephropathies - metabolism Diabetic Nephropathies - pathology Disease Progression Human Physiology Humans Internal Medicine Kidney Diseases - metabolism Kidney Diseases - pathology Medicine Medicine & Public Health Metabolic Diseases Proteome - metabolism Proteomics - methods Review |
| title | Proteomics for prediction of disease progression and response to therapy in diabetic kidney disease |
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