Urinary peptidomic liquid biopsy for non-invasive differential diagnosis of chronic kidney disease.
Mavrogeorgis E, He T, Mischak H, Latosinska A, Vlahou A, Schanstra JP, Catanese L, Amann KU, Huber TB, Beige J, Rupprecht HD, Siwy J (2024)
Publication Type: Journal article
Publication year: 2024
Journal
Book Volume: 39
Pages Range: 453-462
Journal Issue: 3
DOI: 10.1093/ndt/gfad200
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.
Authors with CRIS profile
Involved external institutions
mosaiques diagnostics and therapeutics AG
Germany (DE)
Klinikum St. Georg
Germany (DE)
Biomedical Research Foundation of the Academy of Athens (BRFAA) / Iδρυμα Ιατροβιολογικών Ερευνών της Ακαδημίας Αθηνών (ΙΙΒΕΑΑ)
Greece (GR)
Klinikum Bayreuth
Germany (DE)
Universitätsklinikum Hamburg-Eppendorf (UKE)
Germany (DE)
National Institute for Health and Medical Research / Institut national de la santé et de la recherche médicale (INSERM)
France (FR)
Germany (DE)
Klinikum St. Georg
Germany (DE)
Biomedical Research Foundation of the Academy of Athens (BRFAA) / Iδρυμα Ιατροβιολογικών Ερευνών της Ακαδημίας Αθηνών (ΙΙΒΕΑΑ)
Greece (GR)
Klinikum Bayreuth
Germany (DE)
Universitätsklinikum Hamburg-Eppendorf (UKE)
Germany (DE)
National Institute for Health and Medical Research / Institut national de la santé et de la recherche médicale (INSERM)
France (FR)
How to cite
APA:
Mavrogeorgis, E., He, T., Mischak, H., Latosinska, A., Vlahou, A., Schanstra, J.P.,... Siwy, J. (2024). Urinary peptidomic liquid biopsy for non-invasive differential diagnosis of chronic kidney disease. Nephrology Dialysis Transplantation, 39(3), 453-462. https://doi.org/10.1093/ndt/gfad200
MLA:
Mavrogeorgis, Emmanouil, et al. "Urinary peptidomic liquid biopsy for non-invasive differential diagnosis of chronic kidney disease." Nephrology Dialysis Transplantation 39.3 (2024): 453-462.
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