학술논문
Urinary peptidomic liquid biopsy for non-invasive differential diagnosis of chronic kidney disease.
Document Type
Academic Journal
Author
Mavrogeorgis E; Mosaiques Diagnostics GmbH, Hannover, Germany.; Institute for Molecular Cardiovascular Research (IMCAR), RWTH Aachen University Hospital, Aachen, Germany.; He T; Mosaiques Diagnostics GmbH, Hannover, Germany.; Mischak H; Mosaiques Diagnostics GmbH, Hannover, Germany.; Latosinska A; Mosaiques Diagnostics GmbH, Hannover, Germany.; Vlahou A; Center of Systems Biology, Biomedical Research Foundation of the Academy of Athens, Athens, Greece.; Schanstra JP; Institut National de la Santé et de la Recherche Médicale (INSERM), U1297, Institute of Cardiovascular and Metabolic Disease, Toulouse, France.; Université Toulouse III Paul-Sabatier, Toulouse, France.; Catanese L; Department of Nephrology, Angiology and Rheumatology, Klinikum Bayreuth GmbH, Bayreuth, Germany.; Kuratorium for Dialysis and Transplantation (KfH) Bayreuth, Bayreuth, Germany.; Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany.; Amann K; Department of Nephropathology, Institute of Pathology, Friedrich-Alexander-University of Erlangen-Nürnberg, Erlangen, Germany.; Huber TB; III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.; Hamburg Center for Kidney Health (HCKH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany.; Beige J; Department of Infectious Diseases/Tropical Medicine, Nephrology/KfH Renal Unit and Rheumatology, St Georg Hospital Leipzig, Leipzig, Germany.; Kuratorium for Dialysis and Transplantation (KfH) Renal Unit, St Georg Hospital, Leipzig, Germany.; Department of Internal Medicine II, Martin-Luther-University Halle/Wittenberg, Halle (Saale), Germany.; Rupprecht HD; Department of Nephrology, Angiology and Rheumatology, Klinikum Bayreuth GmbH, Bayreuth, Germany.; Kuratorium for Dialysis and Transplantation (KfH) Bayreuth, Bayreuth, Germany.; Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany.; Siwy J; Mosaiques Diagnostics GmbH, Hannover, Germany.
Source
Publisher: Oxford University Press Country of Publication: England NLM ID: 8706402 Publication Model: Print Cited Medium: Internet ISSN: 1460-2385 (Electronic) Linking ISSN: 09310509 NLM ISO Abbreviation: Nephrol Dial Transplant Subsets: MEDLINE
Subject
Language
English
Abstract
Background and Hypothesis: Specific urinary peptides hold information on disease pathophysiology, which, in combination with artificial intelligence, could enable non-invasive assessment of chronic kidney disease (CKD) aetiology. Existing approaches are generally specific for the diagnosis of single aetiologies. We present the development of models able to simultaneously distinguish and spatially visualize multiple CKD aetiologies.
Methods: The urinary peptide data of 1850 healthy control (HC) and CKD [diabetic kidney disease (DKD), immunoglobulin A nephropathy (IgAN) and vasculitis] participants were extracted from the Human Urinary Proteome Database. Uniform manifold approximation and projection (UMAP) coupled to a support vector machine algorithm was used to generate multi-peptide models to perform binary (DKD, HC) and multiclass (DKD, HC, IgAN, vasculitis) classifications. This pipeline was compared with the current state-of-the-art single-aetiology CKD urinary peptide models.
Results: In an independent test set, the developed models achieved 90.35% and 70.13% overall predictive accuracies, respectively, for the binary and the multiclass classifications. Omitting the UMAP step led to improved predictive accuracies (96.14% and 85.06%, respectively). As expected, the HC class was distinguished with the highest accuracy. The different classes displayed a tendency to form distinct clusters in the 3D space based on their disease state.
Conclusion: Urinary peptide data present an effective basis for CKD aetiology differentiation using machine learning models. Although adding the UMAP step to the models did not improve prediction accuracy, it may provide a unique visualization advantage. Additional studies are warranted to further validate the pipeline's clinical potential as well as to expand it to other CKD aetiologies and also other diseases.
(© The Author(s) 2023. Published by Oxford University Press on behalf of the ERA.)
Methods: The urinary peptide data of 1850 healthy control (HC) and CKD [diabetic kidney disease (DKD), immunoglobulin A nephropathy (IgAN) and vasculitis] participants were extracted from the Human Urinary Proteome Database. Uniform manifold approximation and projection (UMAP) coupled to a support vector machine algorithm was used to generate multi-peptide models to perform binary (DKD, HC) and multiclass (DKD, HC, IgAN, vasculitis) classifications. This pipeline was compared with the current state-of-the-art single-aetiology CKD urinary peptide models.
Results: In an independent test set, the developed models achieved 90.35% and 70.13% overall predictive accuracies, respectively, for the binary and the multiclass classifications. Omitting the UMAP step led to improved predictive accuracies (96.14% and 85.06%, respectively). As expected, the HC class was distinguished with the highest accuracy. The different classes displayed a tendency to form distinct clusters in the 3D space based on their disease state.
Conclusion: Urinary peptide data present an effective basis for CKD aetiology differentiation using machine learning models. Although adding the UMAP step to the models did not improve prediction accuracy, it may provide a unique visualization advantage. Additional studies are warranted to further validate the pipeline's clinical potential as well as to expand it to other CKD aetiologies and also other diseases.
(© The Author(s) 2023. Published by Oxford University Press on behalf of the ERA.)