Proteome‐base biomarkers in diabetes mellitus: Progress on biofluids' protein profiling using mass spectrometry
Identifieur interne : 003811 ( Main/Exploration ); précédent : 003810; suivant : 003812Proteome‐base biomarkers in diabetes mellitus: Progress on biofluids' protein profiling using mass spectrometry
Auteurs : Ana Isabel Padrão [Portugal] ; Rita Ferreira [Portugal] ; Rui Vitorino [Portugal] ; Francisco Amado [Portugal]Source :
- PROTEOMICS – Clinical Applications [ 1862-8346 ] ; 2012-10.
Descripteurs français
- Wicri :
- topic : Diabète, Glucose, Spectrométrie.
English descriptors
- KwdEn :
- Abundant proteins, Appl, Biomarker, Biomarker discovery, Biomarkers, Cell proteome, Cell proteomics, Cellular processes, Chronic hyperglycemia, Chronic kidney disease, Clin, Clusterin, Collagen fragments, Complement factor, Complication, Diabetes, Diabetes care, Diabetes mellitus, Diabetes mellitus type, Diabetes patients, Diabetic, Diabetic macular edema, Diabetic nephropathy, Diabetic patients, Diabetic retinopathy, Differential expression, Differentially, Dmwr, Downregulated, Elisa, Glucose, Gmbh, Growth factor beta, Haptoglobin, Healthy controls, Healthy individuals, Higher levels, Immunoglobulin, Kgaa, Large number, Lower levels, Macular, Mass spectrometry, Mellitus, Microalbuminuric patients, Nephropathy, Nondiabetic, Nondiabetic individuals, Npdr, Oral pathologies, Padrao, Pedf, Peptide, Polypeptide, Potential biomarkers, Precursor, Proliferative, Promisors biomarkers, Protein, Proteome, Proteome analysis, Proteomic, Proteomic analysis, Proteomics, Proteomics clin, Receptor, Renal, Renal function, Retinopathy, Salivary, Serum proteins, Spectrometry, Transthyretin, Uids, Upregulated, Urinary, Urinary proteomics, Urine, Validation, Verlag, Verlag gmbh, Vitamin protein, Vitreous, Vitreous proteome, Vitreous samples, Weinheim, Weinheim proteomics clin.
- Teeft :
- Abundant proteins, Appl, Biomarker, Biomarker discovery, Biomarkers, Cell proteome, Cell proteomics, Cellular processes, Chronic hyperglycemia, Chronic kidney disease, Clin, Clusterin, Collagen fragments, Complement factor, Complication, Diabetes, Diabetes care, Diabetes mellitus, Diabetes mellitus type, Diabetes patients, Diabetic, Diabetic macular edema, Diabetic nephropathy, Diabetic patients, Diabetic retinopathy, Differential expression, Differentially, Dmwr, Downregulated, Elisa, Glucose, Gmbh, Growth factor beta, Haptoglobin, Healthy controls, Healthy individuals, Higher levels, Immunoglobulin, Kgaa, Large number, Lower levels, Macular, Mass spectrometry, Mellitus, Microalbuminuric patients, Nephropathy, Nondiabetic, Nondiabetic individuals, Npdr, Oral pathologies, Padrao, Pedf, Peptide, Polypeptide, Potential biomarkers, Precursor, Proliferative, Promisors biomarkers, Protein, Proteome, Proteome analysis, Proteomic, Proteomic analysis, Proteomics, Proteomics clin, Receptor, Renal, Renal function, Retinopathy, Salivary, Serum proteins, Spectrometry, Transthyretin, Uids, Upregulated, Urinary, Urinary proteomics, Urine, Validation, Verlag, Verlag gmbh, Vitamin protein, Vitreous, Vitreous proteome, Vitreous samples, Weinheim, Weinheim proteomics clin.
Abstract
The worldwide number of individuals suffering from diabetes mellitus (DM) has been projected to rise from 171 million in 2000 to 366 million in 2030. Identification of specific biomarkers for prediction and monitoring of DM is needed not only for the adequate screening diagnosis but also to assist the design of interventions to prevent or delay progression of this pathology and its attendant complications. Proteomic methods based on MS hold special promise for the identification of novel biomarkers that might form the foundation for new clinical tests, but to date, their contribution has been somehow unfruitful. Indeed, from more than 300 proteins found differently modulated in body fluids from diabetic patients, approximately 50 were validated with other approaches like ELISA or Western blotting and the clinical trials are being initiated to employ biofluids’ proteomics (specifically urinary proteomics) in clinical decision. This review provides an overview of MS‐based applications in the identification of potential biomarkers for DM, emphasizing the methodological challenges involved.
Url:
DOI: 10.1002/prca.201200044
Affiliations:
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unit="page-count">20</biblScope><date type="published" when="2012-10">2012-10</date></imprint><idno type="ISSN">1862-8346</idno></series></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">1862-8346</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Abundant proteins</term><term>Appl</term><term>Biomarker</term><term>Biomarker discovery</term><term>Biomarkers</term><term>Cell proteome</term><term>Cell proteomics</term><term>Cellular processes</term><term>Chronic hyperglycemia</term><term>Chronic kidney disease</term><term>Clin</term><term>Clusterin</term><term>Collagen fragments</term><term>Complement factor</term><term>Complication</term><term>Diabetes</term><term>Diabetes care</term><term>Diabetes mellitus</term><term>Diabetes mellitus type</term><term>Diabetes patients</term><term>Diabetic</term><term>Diabetic macular edema</term><term>Diabetic nephropathy</term><term>Diabetic patients</term><term>Diabetic retinopathy</term><term>Differential expression</term><term>Differentially</term><term>Dmwr</term><term>Downregulated</term><term>Elisa</term><term>Glucose</term><term>Gmbh</term><term>Growth factor beta</term><term>Haptoglobin</term><term>Healthy controls</term><term>Healthy individuals</term><term>Higher levels</term><term>Immunoglobulin</term><term>Kgaa</term><term>Large number</term><term>Lower levels</term><term>Macular</term><term>Mass spectrometry</term><term>Mellitus</term><term>Microalbuminuric patients</term><term>Nephropathy</term><term>Nondiabetic</term><term>Nondiabetic individuals</term><term>Npdr</term><term>Oral pathologies</term><term>Padrao</term><term>Pedf</term><term>Peptide</term><term>Polypeptide</term><term>Potential biomarkers</term><term>Precursor</term><term>Proliferative</term><term>Promisors biomarkers</term><term>Protein</term><term>Proteome</term><term>Proteome analysis</term><term>Proteomic</term><term>Proteomic analysis</term><term>Proteomics</term><term>Proteomics clin</term><term>Receptor</term><term>Renal</term><term>Renal function</term><term>Retinopathy</term><term>Salivary</term><term>Serum proteins</term><term>Spectrometry</term><term>Transthyretin</term><term>Uids</term><term>Upregulated</term><term>Urinary</term><term>Urinary proteomics</term><term>Urine</term><term>Validation</term><term>Verlag</term><term>Verlag gmbh</term><term>Vitamin protein</term><term>Vitreous</term><term>Vitreous proteome</term><term>Vitreous samples</term><term>Weinheim</term><term>Weinheim proteomics clin</term></keywords><keywords scheme="Teeft" xml:lang="en"><term>Abundant proteins</term><term>Appl</term><term>Biomarker</term><term>Biomarker discovery</term><term>Biomarkers</term><term>Cell proteome</term><term>Cell proteomics</term><term>Cellular processes</term><term>Chronic hyperglycemia</term><term>Chronic kidney disease</term><term>Clin</term><term>Clusterin</term><term>Collagen fragments</term><term>Complement factor</term><term>Complication</term><term>Diabetes</term><term>Diabetes care</term><term>Diabetes mellitus</term><term>Diabetes mellitus type</term><term>Diabetes patients</term><term>Diabetic</term><term>Diabetic macular edema</term><term>Diabetic nephropathy</term><term>Diabetic patients</term><term>Diabetic retinopathy</term><term>Differential expression</term><term>Differentially</term><term>Dmwr</term><term>Downregulated</term><term>Elisa</term><term>Glucose</term><term>Gmbh</term><term>Growth factor beta</term><term>Haptoglobin</term><term>Healthy controls</term><term>Healthy individuals</term><term>Higher levels</term><term>Immunoglobulin</term><term>Kgaa</term><term>Large number</term><term>Lower levels</term><term>Macular</term><term>Mass spectrometry</term><term>Mellitus</term><term>Microalbuminuric patients</term><term>Nephropathy</term><term>Nondiabetic</term><term>Nondiabetic individuals</term><term>Npdr</term><term>Oral pathologies</term><term>Padrao</term><term>Pedf</term><term>Peptide</term><term>Polypeptide</term><term>Potential biomarkers</term><term>Precursor</term><term>Proliferative</term><term>Promisors biomarkers</term><term>Protein</term><term>Proteome</term><term>Proteome analysis</term><term>Proteomic</term><term>Proteomic analysis</term><term>Proteomics</term><term>Proteomics clin</term><term>Receptor</term><term>Renal</term><term>Renal function</term><term>Retinopathy</term><term>Salivary</term><term>Serum proteins</term><term>Spectrometry</term><term>Transthyretin</term><term>Uids</term><term>Upregulated</term><term>Urinary</term><term>Urinary proteomics</term><term>Urine</term><term>Validation</term><term>Verlag</term><term>Verlag gmbh</term><term>Vitamin protein</term><term>Vitreous</term><term>Vitreous proteome</term><term>Vitreous samples</term><term>Weinheim</term><term>Weinheim proteomics clin</term></keywords><keywords scheme="Wicri" type="topic" xml:lang="fr"><term>Diabète</term><term>Glucose</term><term>Spectrométrie</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract">The worldwide number of individuals suffering from diabetes mellitus (DM) has been projected to rise from 171 million in 2000 to 366 million in 2030. Identification of specific biomarkers for prediction and monitoring of DM is needed not only for the adequate screening diagnosis but also to assist the design of interventions to prevent or delay progression of this pathology and its attendant complications. Proteomic methods based on MS hold special promise for the identification of novel biomarkers that might form the foundation for new clinical tests, but to date, their contribution has been somehow unfruitful. Indeed, from more than 300 proteins found differently modulated in body fluids from diabetic patients, approximately 50 were validated with other approaches like ELISA or Western blotting and the clinical trials are being initiated to employ biofluids’ proteomics (specifically urinary proteomics) in clinical decision. This review provides an overview of MS‐based applications in the identification of potential biomarkers for DM, emphasizing the methodological challenges involved.</div></front></TEI><affiliations><list><country><li>Portugal</li></country></list><tree><country name="Portugal"><noRegion><name sortKey="Isabel Padrao, Ana" sort="Isabel Padrao, Ana" uniqKey="Isabel Padrao A" first="Ana" last="Isabel Padrão">Ana Isabel Padrão</name></noRegion><name sortKey="Amado, Francisco" sort="Amado, Francisco" uniqKey="Amado F" first="Francisco" last="Amado">Francisco Amado</name><name sortKey="Ferreira, Rita" sort="Ferreira, Rita" uniqKey="Ferreira R" first="Rita" last="Ferreira">Rita Ferreira</name><name sortKey="Vitorino, Rui" sort="Vitorino, Rui" uniqKey="Vitorino R" first="Rui" last="Vitorino">Rui Vitorino</name><name sortKey="Vitorino, Rui" sort="Vitorino, Rui" uniqKey="Vitorino R" first="Rui" last="Vitorino">Rui Vitorino</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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