{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T11:03:49Z","timestamp":1764587029307,"version":"build-2065373602"},"reference-count":42,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,9,28]],"date-time":"2022-09-28T00:00:00Z","timestamp":1664323200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Poznan University of Medical Sciences"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJERPH"],"abstract":"<jats:p>Tonometry is commonly used to provide efficient and good diagnostics for cardiovascular disease (CVD). There are many advantages of this method, including low cost, non-invasiveness and little time to perform. In this study, the effort was undertaken to check whether tonometry data hides valuable information associated with different stages of chronic kidney disease (CKD) and end-stage renal disease (ESRD) treatment. For this purpose, six groups containing patients at different stages of CKD following different ways of dialysis treatment, as well as patients without CKD but with CVD and healthy volunteers were assessed. It was revealed that each of the studied groups had a unique profile. Only the type of dialysis was indistinguishable a from tonometric perspective (hemodialysis vs. peritoneal dialysis). Several techniques were used to build profiles that independently gave the same outcome: analysis of variance, network correlation structure analysis, multinomial logistic regression, and discrimination analysis. Moreover, to evaluate the classification potential of the discriminatory model, all mentioned techniques were later compared and treated as feature selection methods. Although the results are promising, it could be difficult to express differences as simple mathematical relations. This study shows that artificial intelligence can differentiate between different stages of CKD and patients without CKD. Potential future machine learning models will be able to determine kidney health with high accuracy and thereby classify patients. ClinicalTrials.gov Identifier: NCT05214872.<\/jats:p>","DOI":"10.3390\/ijerph191912339","type":"journal-article","created":{"date-parts":[[2022,9,28]],"date-time":"2022-09-28T20:58:47Z","timestamp":1664398727000},"page":"12339","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Chronic Kidney Disease as a Cardiovascular Disorder\u2014Tonometry Data Analyses"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0661-0128","authenticated-orcid":false,"given":"Mateusz","family":"Twardawa","sequence":"first","affiliation":[{"name":"Institute of Computing Science, Poznan University of Technology, 60-965 Poznan, Poland"},{"name":"ICT Security Department, Poznan Supercomputing and Networking Center Affiliated to the Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-139 Poznan, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2719-3677","authenticated-orcid":false,"given":"Piotr","family":"Formanowicz","sequence":"additional","affiliation":[{"name":"Institute of Computing Science, Poznan University of Technology, 60-965 Poznan, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6691-3863","authenticated-orcid":false,"given":"Dorota","family":"Formanowicz","sequence":"additional","affiliation":[{"name":"Department of Medical Chemistry and Laboratory Medicine, Poznan University of Medical Sciences, 60-806 Poznan, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1157","DOI":"10.1161\/CIRCULATIONAHA.120.050686","article-title":"Cardiovascular Disease in Chronic Kidney Disease","volume":"143","author":"Jankowski","year":"2021","journal-title":"Circulation"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Podkowi\u0144ska, A., and Formanowicz, D. (2020). Chronic Kidney Disease as Oxidative Stress- and Inflammatory-Mediated Cardiovascular Disease. Antioxidants, 9.","DOI":"10.3390\/antiox9080752"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"907","DOI":"10.1007\/s11255-011-9947-4","article-title":"Transferrin changes in haemodialysed patients","volume":"44","author":"Formanowicz","year":"2012","journal-title":"Int. Urol. Nephrol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1007\/s00449-010-0507-6","article-title":"Some aspects of the anemia of chronic disorders modeled and analyzed by petri net based approach","volume":"34","author":"Formanowicz","year":"2011","journal-title":"Bioprocess. Biosyst. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"937398","DOI":"10.1155\/2014\/937398","article-title":"Heart Failure in Patients with Chronic Kidney Disease: A Systematic Integrative Review","volume":"2014","author":"Segall","year":"2014","journal-title":"BioMed Res. Int."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Kasprzak, \u0141., Twardawa, M., Formanowicz, P., and Formanowicz, D. (2022). The Mutual Contribution of 3-NT, IL-18, Albumin, and Phosphate Foreshadows Death of Hemodialyzed Patients in a 2-Year Follow-Up. Antioxidants, 11.","DOI":"10.3390\/antiox11020355"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1007\/s40265-019-1064-1","article-title":"Management of Hypertension in Chronic Kidney Disease","volume":"79","author":"Pugh","year":"2019","journal-title":"Drugs"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1138","DOI":"10.1161\/HYPERTENSIONAHA.113.02001","article-title":"Randomized Trial of Guiding Hypertension Management Using Central Aortic Blood Pressure Compared With Best-Practice Care","volume":"62","author":"Sharman","year":"2013","journal-title":"Hypertension"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1016\/j.kint.2016.05.014","article-title":"Estimated aortic blood pressure based on radial artery tonometry underestimates directly measured aortic blood pressure in patients with advancing chronic kidney disease staging and increasing arterial stiffness","volume":"90","author":"Carlsen","year":"2016","journal-title":"Kidney Int."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"736","DOI":"10.1016\/j.kint.2016.06.039","article-title":"Estimating central blood pressure in the extreme vascular phenotype of advanced kidney disease","volume":"90","author":"Boutouyrie","year":"2016","journal-title":"Kidney Int."},{"key":"ref_11","unstructured":"Mika, S., Ratsch, G., Weston, J., Scholkopf, B., and Mullers, K. (1999, January 25). Fisher discriminant analysis with kernels. Proceedings of the Neural Networks for Signal Processing IX: 1999 IEEE Signal Processing Society Workshop, Madison, WI, USA."},{"key":"ref_12","unstructured":"Ghojogh, B., Karray, F., and Crowley, M. (2019). Fisher and Kernel Fisher Discriminant Analysis: Tutorial. arXiv."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1038\/s41592-019-0686-2","article-title":"SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python","volume":"17","author":"Virtanen","year":"2020","journal-title":"Nat. Methods"},{"key":"ref_14","unstructured":"Hagberg, A., Swart, P., and Chult, D.S. (2008). Exploring Network Structure, Dynamics, and Function Using NetworkX, Los Alamos National Lab. (LANL). Technical Report."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Venables, W.N., and Ripley, B.D. (2002). Modern Applied Statistics with S, Springer. [4th ed.].","DOI":"10.1007\/978-0-387-21706-2"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1686","DOI":"10.21105\/joss.01686","article-title":"Welcome to the tidyverse","volume":"4","author":"Wickham","year":"2019","journal-title":"J. Open Source Softw."},{"key":"ref_17","unstructured":"Nikomborirak, K. (2022, February 03). Kernel FDA (0.1.1). Available online: https:\/\/github.com\/concavegit\/kfda."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1038\/s41586-020-2649-2","article-title":"Array programming with NumPy","volume":"585","author":"Harris","year":"2020","journal-title":"Nature"},{"key":"ref_19","unstructured":"(2022, February 03). Pandas Development Team Pandas-Dev\/Pandas: Pandas, 2020. Available online: https:\/\/pandas.pydata.org\/."},{"key":"ref_20","unstructured":"van der Walt, S., and Jarrod, M. (July, January 28). Data Structures for Statistical Computing in Python. Proceedings of the 9th Python in Science Conference, Austin, TX, USA."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/MCSE.2007.55","article-title":"Matplotlib: A 2D graphics environment","volume":"9","author":"Hunter","year":"2007","journal-title":"Comput. Sci. Eng."},{"key":"ref_22","unstructured":"Caswell, T.A., Droettboom, M., Lee, A., Hunter, J., Firing, E., Stansby, D., Klymak, J., de Andrade, E.S., Hoffmann, T., and Nielsen, J.H. (2022, February 03). Matplotlib\/Matplotlib v3.1.2. Available online: https:\/\/matplotlib.org\/."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3021","DOI":"10.21105\/joss.03021","article-title":"Seaborn: Statistical data visualization","volume":"6","author":"Waskom","year":"2021","journal-title":"J. Open Source Softw."},{"key":"ref_24","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1007\/s11042-015-3058-7","article-title":"Dual-source discrimination power analysis for multi-instance contactless palmprint recognition","volume":"76","author":"Leng","year":"2017","journal-title":"Multimed. Tools Appl."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Tam, J.C.W., Ko, C.H., Koon, C.M., Cheng, Z., Lok, W.H., Lau, C.P., Leung, P.C., Fung, K.P., Chan, W.Y., and Lau, C.B.S. (2015). Identification of Target Genes Involved in Wound Healing Angiogenesis of Endothelial Cells with the Treatment of a Chinese 2-Herb Formula. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0139342"},{"key":"ref_27","unstructured":"Wang, W., Guan, X., and Zhang, X. (2004, January 14\u201317). Profiling program and user behaviors for anomaly intrusion detection based on non-negative matrix factorization. Proceedings of the 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601), Nassau, Bahamas."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5416","DOI":"10.1038\/s41467-019-13056-x","article-title":"The art of using t-SNE for single-cell transcriptomics","volume":"10","author":"Kobak","year":"2019","journal-title":"Nat. Commun."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Tian, Q., Zou, J., Tang, J., and Fan, S. (2021, January 13\u201315). Multi-distance based spectral embedding fusion for clustering single-cell methylation data. Proceedings of the 2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), Melbourne, Australia.","DOI":"10.1109\/CIBCB49929.2021.9562895"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"5985479","DOI":"10.1155\/2017\/5985479","article-title":"Comparison of Machine Learning Methods for the Arterial Hypertension Diagnostics","volume":"2017","author":"Kublanov","year":"2017","journal-title":"Appl. Bionics Biomech."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2097","DOI":"10.1007\/s10237-021-01497-7","article-title":"Machine learning for detection of stenoses and aneurysms: Application in a physiologically realistic virtual patient database","volume":"20","author":"Jones","year":"2021","journal-title":"Biomech. Model. Mechanobiol."},{"key":"ref_32","first-page":"1573","article-title":"Pulse Wave Velocity and Machine Learning to Predict Cardiovascular Outcomes in Prediabetic and Diabetic Populations","volume":"44","year":"2020","journal-title":"J. Med. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"82","DOI":"10.3390\/jpm3020082","article-title":"Machine Learning Techniques for Arterial Pressure Waveform Analysis","volume":"3","author":"Almeida","year":"2013","journal-title":"J. Pers. Med."},{"key":"ref_34","unstructured":"Nabeel, P., Vinay, C., Kiran, V.R., Jayaraj, J., and Mohanasankar, S. (2019, January 26\u201328). Deep Learning for Blood Pressure Estimation: An Approach using Local Measure of Arterial Dual Diameter Waveforms. Proceedings of the 2019 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Istanbul, Turkey."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Chen, S., Ji, Z., Wu, H., and Xu, Y. (2019). A Non-Invasive Continuous Blood Pressure Estimation Approach Based on Machine Learning. Sensors, 19.","DOI":"10.3390\/s19112585"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2052","DOI":"10.1038\/sdata.2016.35","article-title":"MIMIC-III, a freely accessible critical care database","volume":"3","author":"Johnson","year":"2016","journal-title":"Sci. Data"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Athaya, T., and Choi, S. (2022). A Review of Noninvasive Methodologies to Estimate the Blood Pressure Waveform. Sensors, 22.","DOI":"10.3390\/s22103953"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1958","DOI":"10.3389\/fmed.2021.765924","article-title":"Chronic Kidney Disease and Arterial Stiffness: A Two-Way Path","volume":"8","author":"Inserra","year":"2021","journal-title":"Front. Med."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1394","DOI":"10.1136\/bjophthalmol-2019-315446","article-title":"Development and validation of a machine learning, smartphone-based tonometer","volume":"104","author":"Wu","year":"2020","journal-title":"Br. J. Ophthalmol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2316","DOI":"10.1093\/ckj\/sfab080","article-title":"Point-of-care testing technologies for the home in chronic kidney disease: A narrative review","volume":"14","author":"Bodington","year":"2021","journal-title":"Clin. Kidney J."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1515\/fcds-2017-0018","article-title":"3D Blood Vessels Reconstruction Based on Segmented CT Data for Further Simulations of Hemodynamic in Human Artery Branches","volume":"42","author":"Strzelecki","year":"2017","journal-title":"Found. Comput. Decis. Sci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/S0140-6736(13)60687-X","article-title":"Chronic kidney disease: Global dimension and perspectives","volume":"382","author":"Jha","year":"2013","journal-title":"Lancet"}],"container-title":["International Journal of Environmental Research and Public Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1660-4601\/19\/19\/12339\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:41:29Z","timestamp":1760143289000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1660-4601\/19\/19\/12339"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,28]]},"references-count":42,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["ijerph191912339"],"URL":"https:\/\/doi.org\/10.3390\/ijerph191912339","relation":{},"ISSN":["1660-4601"],"issn-type":[{"type":"electronic","value":"1660-4601"}],"subject":[],"published":{"date-parts":[[2022,9,28]]}}}