Multicentric Validation of Proteomic Biomarkers in Urine Specific for Diabetic Nephropathy
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{"title"=>"Multicentric validation of proteomic biomarkers in urine specific for diabetic Nephropathy", "type"=>"journal", "authors"=>[{"first_name"=>"Alaa", "last_name"=>"Alkhalaff", "scopus_author_id"=>"36622351200"}, {"first_name"=>"Petra", "last_name"=>"Zürbig", "scopus_author_id"=>"12806664300"}, {"first_name"=>"Stephan J.L.", "last_name"=>"Bakker", "scopus_author_id"=>"7006070107"}, {"first_name"=>"Henk J.G.", "last_name"=>"Bilo", "scopus_author_id"=>"7005093236"}, {"first_name"=>"Marie", "last_name"=>"Cerna", "scopus_author_id"=>"35758224100"}, {"first_name"=>"Christine", "last_name"=>"Fischer", "scopus_author_id"=>"7402486599"}, {"first_name"=>"Sebastian", "last_name"=>"Fuchs", "scopus_author_id"=>"36898359100"}, {"first_name"=>"Bart", "last_name"=>"Janssen", "scopus_author_id"=>"7101770057"}, {"first_name"=>"Karel", "last_name"=>"Medek", "scopus_author_id"=>"6504019239"}, {"first_name"=>"Harald", "last_name"=>"Mischak", "scopus_author_id"=>"7004897973"}, {"first_name"=>"Johannes M.", "last_name"=>"Roob", "scopus_author_id"=>"6603399891"}, {"first_name"=>"Kasper", "last_name"=>"Rossing", "scopus_author_id"=>"6603794049"}, {"first_name"=>"Peter", "last_name"=>"Rossing", "scopus_author_id"=>"7005170096"}, {"first_name"=>"Ivan", "last_name"=>"Rychlík", "scopus_author_id"=>"18344680300"}, {"first_name"=>"Harald", "last_name"=>"Sourij", "scopus_author_id"=>"6507809057"}, {"first_name"=>"Beate", "last_name"=>"Tiran", "scopus_author_id"=>"6701732105"}, {"first_name"=>"Brigitte M.", "last_name"=>"Winklhofer-Roob", "scopus_author_id"=>"7004328198"}, {"first_name"=>"Gerjan J.", "last_name"=>"Navis", "scopus_author_id"=>"7006576566"}], "year"=>2010, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"359941748", "sgr"=>"78149479774", "pmid"=>"20975990", "scopus"=>"2-s2.0-78149479774", "isbn"=>"0021620814", "arxiv"=>"NIHMS150003", "doi"=>"10.1371/journal.pone.0013421", "issn"=>"19326203"}, "id"=>"e8caab57-77bd-346f-a9bf-9cf9a56718ec", "abstract"=>"BACKGROUND: Urine proteome analysis is rapidly emerging as a tool for diagnosis and prognosis in disease states. For diagnosis of diabetic nephropathy (DN), urinary proteome analysis was successfully applied in a pilot study. The validity of the previously established proteomic biomarkers with respect to the diagnostic and prognostic potential was assessed on a separate set of patients recruited at three different European centers. In this case-control study of 148 Caucasian patients with diabetes mellitus type 2 and duration ≥5 years, cases of DN were defined as albuminuria >300 mg/d and diabetic retinopathy (n = 66). Controls were matched for gender and diabetes duration (n = 82).\\n\\nMETHODOLOGY/PRINCIPAL FINDINGS: Proteome analysis was performed blinded using high-resolution capillary electrophoresis coupled with mass spectrometry (CE-MS). Data were evaluated employing the previously developed model for DN. Upon unblinding, the model for DN showed 93.8% sensitivity and 91.4% specificity, with an AUC of 0.948 (95% CI 0.898-0.978). Of 65 previously identified peptides, 60 were significantly different between cases and controls of this study. In <10% of cases and controls classification by proteome analysis not entirely resulted in the expected clinical outcome. Analysis of patient's subsequent clinical course revealed later progression to DN in some of the false positive classified DN control patients.\\n\\nCONCLUSIONS: These data provide the first independent confirmation that profiling of the urinary proteome by CE-MS can adequately identify subjects with DN, supporting the generalizability of this approach. The data further establish urinary collagen fragments as biomarkers for diabetes-induced renal damage that may serve as earlier and more specific biomarkers than the currently used urinary albumin.", "link"=>"http://www.mendeley.com/research/multicentric-validation-proteomic-biomarkers-urine-specific-diabetic-nephropathy", "reader_count"=>54, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>3, "Librarian"=>1, "Researcher"=>18, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>8, "Student > Postgraduate"=>1, "Other"=>6, "Student > Master"=>4, "Student > Bachelor"=>5, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>3, "Librarian"=>1, "Researcher"=>18, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>8, "Student > Postgraduate"=>1, "Other"=>6, "Student > Master"=>4, "Student > Bachelor"=>5, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>2}, "reader_count_by_subject_area"=>{"Unspecified"=>6, "Engineering"=>2, "Biochemistry, Genetics and Molecular Biology"=>8, "Medicine and Dentistry"=>15, "Agricultural and Biological Sciences"=>16, "Business, Management and Accounting"=>1, "Chemistry"=>4, "Social Sciences"=>1, "Computer Science"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>2}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>15}, "Chemistry"=>{"Chemistry"=>4}, "Social Sciences"=>{"Social Sciences"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>16}, "Computer Science"=>{"Computer Science"=>1}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>8}, "Unspecified"=>{"Unspecified"=>6}}, "reader_count_by_country"=>{"Canada"=>1, "Austria"=>1, "Netherlands"=>1, "United States"=>2, "Denmark"=>1, "Mexico"=>1}, "group_count"=>6}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/823812"], "description"=>"<p>Displayed is the regulation of alpha-1-antitrypsin fragments, an alpha-2-HS glycoprotein fragment, a beta-2-microglobulin fragment, serum albumin fragments, and a transthyretin fragment. The asterisk (*) indicate same peptide with one more modification (oxidation).</p>", "links"=>[], "tags"=>["derived", "fragments", "urine", "samples", "predictions"], "article_id"=>494145, "categories"=>["Virology", "Medicine", "Infectious Diseases"], "users"=>["Alaa Alkhalaf", "Petra Zürbig", "Stephan J. L. Bakker", "Henk J. G. Bilo", "Marie Cerna", "Christine Fischer", "Sebastian Fuchs", "Bart Janssen", "Karel Medek", "Harald Mischak", "Johannes M. Roob", "Kasper Rossing", "Peter Rossing", "Ivan Rychlik", "Harald Sourij", "Beate Tiran", "Brigitte M. Winklhofer-Roob", "Gerjan J. Navis"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0013421.g005", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Up_regulation_of_blood_derived_protein_fragments_in_urine_samples_of_the_PREDICTIONS_cohort_/494145", "title"=>"Up-regulation of blood derived protein fragments in urine samples of the PREDICTIONS cohort.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-10-20 01:09:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/823304"], "description"=>"<p>The upper panel shows polypeptide patterns of all peptides, which are in the urinary proteome from one patient with (ID: 37908) and one patient without DN (ID: 37907). The lower panel shows distinct peptides of the DN model of these patients urine sample. Each polypeptide is defined by its CE-migration time (x-axis, minutes), mass (y-axis, kDa), and signal intensity (z-axis). The molecular mass is indicated on the left, the normalized migration time is indicated on the bottom.</p>", "links"=>[], "tags"=>["patterns", "exemplarily", "urine"], "article_id"=>493640, "categories"=>["Virology", "Medicine", "Infectious Diseases"], "users"=>["Alaa Alkhalaf", "Petra Zürbig", "Stephan J. L. Bakker", "Henk J. G. Bilo", "Marie Cerna", "Christine Fischer", "Sebastian Fuchs", "Bart Janssen", "Karel Medek", "Harald Mischak", "Johannes M. Roob", "Kasper Rossing", "Peter Rossing", "Ivan Rychlik", "Harald Sourij", "Beate Tiran", "Brigitte M. Winklhofer-Roob", "Gerjan J. Navis"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0013421.g001", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Polypeptide_patterns_of_exemplarily_urine_samples_/493640", "title"=>"Polypeptide patterns of exemplarily urine samples.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-10-20 01:00:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/823651"], "description"=>"<p>Four MS/MS spectra are shown exemplarily of the new identified DN biomarkers, like collagen alpha-1 (I) fragments (<b>A, C</b>), alpha-1-antitrypsin fragment (<b>B</b>), and a collagen alpha-2 (I) fragment (<b>D</b>). The corresponding sequence is displayed above each spectrum (p = hydroxylation of P; k = hydroxylation of K; m = oxidation of M). Identified b-ions are marked in blue and y-ions in red color.</p>", "links"=>[], "tags"=>["spectra"], "article_id"=>493998, "categories"=>["Virology", "Medicine", "Infectious Diseases"], "users"=>["Alaa Alkhalaf", "Petra Zürbig", "Stephan J. L. Bakker", "Henk J. G. Bilo", "Marie Cerna", "Christine Fischer", "Sebastian Fuchs", "Bart Janssen", "Karel Medek", "Harald Mischak", "Johannes M. Roob", "Kasper Rossing", "Peter Rossing", "Ivan Rychlik", "Harald Sourij", "Beate Tiran", "Brigitte M. Winklhofer-Roob", "Gerjan J. Navis"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0013421.g004", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Annotated_fragment_spectra_of_new_identified_peptides_/493998", "title"=>"Annotated fragment spectra of new identified peptides.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-10-20 01:06:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/823427"], "description"=>"<p><b>A</b>) ROC curve and <b>B</b>) Box-Whisker-plot for classification of the ‘case and control’ patient collective with the ‘DN’ pattern are shown.</p>", "links"=>[], "tags"=>["classification"], "article_id"=>493775, "categories"=>["Virology", "Medicine", "Infectious Diseases"], "users"=>["Alaa Alkhalaf", "Petra Zürbig", "Stephan J. L. Bakker", "Henk J. G. Bilo", "Marie Cerna", "Christine Fischer", "Sebastian Fuchs", "Bart Janssen", "Karel Medek", "Harald Mischak", "Johannes M. Roob", "Kasper Rossing", "Peter Rossing", "Ivan Rychlik", "Harald Sourij", "Beate Tiran", "Brigitte M. Winklhofer-Roob", "Gerjan J. Navis"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0013421.g002", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Statistical_analysis_of_the_classification_results_/493775", "title"=>"Statistical analysis of the classification results.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-10-20 01:02:55"}
  • {"files"=>["https://ndownloader.figshare.com/files/823568"], "description"=>"<p>Scatter diagrams of correlation from proteomic biomarker pattern with urinary albumin excretion (UAE) (<b>A</b>) and creatinine clearance (<b>B</b>). The red line shows the regression line with 95% confidence interval (dashed line).</p>", "links"=>[], "tags"=>["biotechnology/protein chemistry and proteomics", "diabetes and endocrinology/type 2 diabetes", "nephrology/chronic kidney disease"], "article_id"=>493912, "categories"=>["Virology", "Medicine", "Infectious Diseases"], "users"=>["Alaa Alkhalaf", "Petra Zürbig", "Stephan J. L. Bakker", "Henk J. G. Bilo", "Marie Cerna", "Christine Fischer", "Sebastian Fuchs", "Bart Janssen", "Karel Medek", "Harald Mischak", "Johannes M. Roob", "Kasper Rossing", "Peter Rossing", "Ivan Rychlik", "Harald Sourij", "Beate Tiran", "Brigitte M. Winklhofer-Roob", "Gerjan J. Navis"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0013421.g003", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Correlation_analysis_/493912", "title"=>"Correlation analysis.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-10-20 01:05:12"}
  • {"files"=>["https://ndownloader.figshare.com/files/410358", "https://ndownloader.figshare.com/files/410409", "https://ndownloader.figshare.com/files/410435", "https://ndownloader.figshare.com/files/410442", "https://ndownloader.figshare.com/files/410453"], "description"=>"<div><h3>Background</h3><p>Urine proteome analysis is rapidly emerging as a tool for diagnosis and prognosis in disease states. For diagnosis of diabetic nephropathy (DN), urinary proteome analysis was successfully applied in a pilot study. The validity of the previously established proteomic biomarkers with respect to the diagnostic and prognostic potential was assessed on a separate set of patients recruited at three different European centers. In this case-control study of 148 Caucasian patients with diabetes mellitus type 2 and duration ≥5 years, cases of DN were defined as albuminuria >300 mg/d and diabetic retinopathy (n = 66). Controls were matched for gender and diabetes duration (n = 82).</p><h3>Methodology/Principal Findings</h3><p>Proteome analysis was performed blinded using high-resolution capillary electrophoresis coupled with mass spectrometry (CE-MS). Data were evaluated employing the previously developed model for DN. Upon unblinding, the model for DN showed 93.8% sensitivity and 91.4% specificity, with an AUC of 0.948 (95% CI 0.898-0.978). Of 65 previously identified peptides, 60 were significantly different between cases and controls of this study. In <10% of cases and controls classification by proteome analysis not entirely resulted in the expected clinical outcome. Analysis of patient's subsequent clinical course revealed later progression to DN in some of the false positive classified DN control patients.</p><h3>Conclusions</h3><p>These data provide the first independent confirmation that profiling of the urinary proteome by CE-MS can adequately identify subjects with DN, supporting the generalizability of this approach. The data further establish urinary collagen fragments as biomarkers for diabetes-induced renal damage that may serve as earlier and more specific biomarkers than the currently used urinary albumin.</p></div>", "links"=>[], "tags"=>["multicentric", "validation", "proteomic", "biomarkers", "urine", "diabetic", "nephropathy"], "article_id"=>141049, "categories"=>["Cancer", "Medicine"], "users"=>["Alaa Alkhalaf", "Petra Zürbig", "Stephan J. L. Bakker", "Henk J. G. Bilo", "Marie Cerna", "Christine Fischer", "Sebastian Fuchs", "Bart Janssen", "Karel Medek", "Harald Mischak", "Johannes M. Roob", "Kasper Rossing", "Peter Rossing", "Ivan Rychlik", "Harald Sourij", "Beate Tiran", "Brigitte M. Winklhofer-Roob", "Gerjan J. Navis"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0013421.s001", "https://dx.doi.org/10.1371/journal.pone.0013421.s002", "https://dx.doi.org/10.1371/journal.pone.0013421.s003", "https://dx.doi.org/10.1371/journal.pone.0013421.s004", "https://dx.doi.org/10.1371/journal.pone.0013421.s005"], "stats"=>{"downloads"=>6, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Multicentric_Validation_of_Proteomic_Biomarkers_in_Urine_Specific_for_Diabetic_Nephropathy/141049", "title"=>"Multicentric Validation of Proteomic Biomarkers in Urine Specific for Diabetic Nephropathy", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2010-10-20 00:17:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/823949"], "description"=>"<p>Displayed is the regulation of a CD99 antigen fragment, different collagen fragments, membrane associated progesterone receptor component 1 fragment, and uromodulin fragment. The hash (#) depicts peptide fragments, which are not significant in this cohort. The asterisk (*) indicate same peptide with one more modification (hydroxylation).</p>", "links"=>[], "tags"=>["further", "peptide", "markers", "urine", "samples", "predictions"], "article_id"=>494287, "categories"=>["Virology", "Medicine", "Infectious Diseases"], "users"=>["Alaa Alkhalaf", "Petra Zürbig", "Stephan J. L. Bakker", "Henk J. G. Bilo", "Marie Cerna", "Christine Fischer", "Sebastian Fuchs", "Bart Janssen", "Karel Medek", "Harald Mischak", "Johannes M. Roob", "Kasper Rossing", "Peter Rossing", "Ivan Rychlik", "Harald Sourij", "Beate Tiran", "Brigitte M. Winklhofer-Roob", "Gerjan J. Navis"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0013421.g006", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Down_Regulation_of_further_peptide_markers_in_urine_samples_of_the_PREDICTIONS_cohort_/494287", "title"=>"(Down-) Regulation of further peptide markers in urine samples of the PREDICTIONS cohort.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-10-20 01:11:27"}
  • {"files"=>["https://ndownloader.figshare.com/files/824174"], "description"=>"<p>M/F: male/female ratio; SBP: systolic blood pressure; DBP: diastolic blood pressure; UAE: urinary albumin excretion; CrCl: creatinine clearance estimated by Cockroft-Gault equation.</p>", "links"=>[], "tags"=>["characteristics", "predictions"], "article_id"=>494525, "categories"=>["Virology", "Medicine", "Infectious Diseases"], "users"=>["Alaa Alkhalaf", "Petra Zürbig", "Stephan J. L. Bakker", "Henk J. G. Bilo", "Marie Cerna", "Christine Fischer", "Sebastian Fuchs", "Bart Janssen", "Karel Medek", "Harald Mischak", "Johannes M. Roob", "Kasper Rossing", "Peter Rossing", "Ivan Rychlik", "Harald Sourij", "Beate Tiran", "Brigitte M. Winklhofer-Roob", "Gerjan J. Navis"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0013421.t001", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Patient_characteristics_means_177_SD_of_the_PREDICTIONS_cohort_/494525", "title"=>"Patient characteristics (means ± SD) of the PREDICTIONS cohort.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-10-20 01:15:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/824078"], "description"=>"<p>ROC analysis for CKD diagnosis of the training set (<b>A</b>) and the test set (<b>B</b>).</p>", "links"=>[], "tags"=>["curves", "classification", "collectives"], "article_id"=>494430, "categories"=>["Virology", "Medicine", "Infectious Diseases"], "users"=>["Alaa Alkhalaf", "Petra Zürbig", "Stephan J. L. Bakker", "Henk J. G. Bilo", "Marie Cerna", "Christine Fischer", "Sebastian Fuchs", "Bart Janssen", "Karel Medek", "Harald Mischak", "Johannes M. Roob", "Kasper Rossing", "Peter Rossing", "Ivan Rychlik", "Harald Sourij", "Beate Tiran", "Brigitte M. Winklhofer-Roob", "Gerjan J. Navis"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0013421.g007", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_ROC_curves_for_classification_of_the_patient_collectives_with_the_8216_DN_type_2_8217_pattern_/494430", "title"=>"ROC curves for classification of the patient collectives with the ‘DN type 2’ pattern.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-10-20 01:13:50"}

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