Proteomics-Based Machine Learning Approach as an Alternative to Conventional Biomarkers for Differential Diagnosis of Chronic Kidney Diseases
Open accessAbstract
Diabetic nephropathy, hypertension, and glomerulonephritis are the most common causes of chronic kidney diseases (CKD). Since CKD of various origins may not become apparent until kidney function is significantly impaired, a differential diagnosis and an appropriate treatment are needed at the very early stages. Conventional biomarkers may not have sufficient separation capabilities, while a full-proteomic approach may be used for these purposes. In the current study, several machine learning algorithms were examined for the differential diagnosis of CKD of three origins. The tested dataset was based on whole proteomic data obtained after the mass spectrometric analysis of plasma and urine samples of 34 CKD patients and the use of label-free quantification approach. The k-nearest-neighbors algorithm showed the possibility of separation of a healthy group from renal patients in general by proteomics data of plasma with high confidence (97.8%). This algorithm has also be proven to be the best of the three tested for distinguishing the groups of patients with diabetic nephropathy and glomerulonephritis according to proteomics data of plasma (96.3% of correct decisions). The group of hypertensive nephropathy could not be reliably separated according to plasma data, whereas analysis of entire proteomics data of urine did not allow differentiating the three diseases. Nevertheless, the group of hypertensive nephropathy was reliably separated from all other renal patients using the k-nearest-neighbors classifier “one against all” with 100% of accuracy by urine proteome data. The tested algorithms show good abilities to differentiate the various groups across proteomic data sets, which may help to avoid invasive intervention for the verification of the glomerulonephritis subtypes, as well as to differentiate hypertensive and diabetic nephropathy in the early stages based not on individual biomarkers, but on the whole proteomic composition of urine and blood.
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Proteomics-based models enabled early discrimination among CKD etiologies, suggesting utility for non-invasive DN diagnosis.
“Proteomics-based models further enabled early discrimination among CKD etiologies, suggesting utility for non-invasive DN diagnosis [80].”
From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [80]
The Discussion reports that plasma-proteomic models distinguished diabetic nephropathy from glomerulonephritis, and the Conclusions suggest that proteomics-based machine learning may help diagnose diabetic nephropathy at an early stage. But the Discussion describes the tested patients as having previously diagnosed renal disease; the study does not establish early-stage discrimination among CKD etiologies. (LLM, conf=0.88)
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- Authors
- Glazyrin, Y. E., Veprintsev, D. V., Ler, I. A., Rossovskaya, M. L., Varygina, S. A., Glizer, S. L., Zamay, T. N., Petrova, M. M., Minic, Z., Berezovski, M. V., & Kichkailo, A. S.
- Affiliations
- Krasnoyarsk Scientific Center (RU)
- Krasnoyarsk Regional Clinical Hospital (RU)
- Krasnoyarsk State Medical University (RU)
- Faculty of Medicine, Krasnoyarsk State Medical University Named after Prof. V.F. Voyno-Yasenetsky, 660022 Krasnoyarsk, Russia
- Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, ON K1N6N5, Canada
- University of Ottawa (CA)
- Corresponding author
- Glazyrin, Yury E.
- Venue
- International Journal of Molecular Sciences
- Publisher
- MDPI AG
- Published
- 2020-07-07
- Volume
- 21
- Issue
- 13
- Pages
- 4802
- ISSN
- 1422-0067 (ISSN-L)
- PMC
- PMC7369970
- OpenAlex
- W3039863359
- Cited by
- 35
- References
- 30
- Citations by year
- 2026: 5 · 2025: 5 · 2024: 4 · 2023: 10 · 2022: 5 · 2021: 5 · 2020: 1
- FWCI
- 3.30
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- International Journal of Molecular Sciences (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Nephrology › Chronic Kidney Disease and Diabetes
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- chronic kidney disease, differential diagnosis, diabetic nephropathy, hypertensive nephropathy, glomerulonephritis, plasma proteomics, urine proteomics, label-free quantification, k-nearest neighbors, machine learning classification, early diagnosis, biomarkers
- MeSH terms
- Machine Learning, Diagnosis, Differential, Female, Humans, Kidney, Male, Middle Aged, Biomarkers, Proteome, Proteomics, Renal Insufficiency, Chronic
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