{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T22:07:26Z","timestamp":1783030046894,"version":"3.54.6"},"reference-count":26,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T00:00:00Z","timestamp":1758153600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Stefan cel Mare University of Suceava","award":["CNFIS-FDI-2025-F-0603"],"award-info":[{"award-number":["CNFIS-FDI-2025-F-0603"]}]},{"name":"Romania National Council for Higher Education Funding","award":["CNFIS-FDI-2025-F-0603"],"award-info":[{"award-number":["CNFIS-FDI-2025-F-0603"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biosensors"],"abstract":"<jats:p>The use of quick and non-invasive techniques for detecting chronic kidney disease (CKD) in patients with type 2 diabetes mellitus is desirable and has recently garnered attention. One of these techniques is the evaluation of nephropathy risk based on electrochemical skin conductance (ESC) measured with a SUDOSCAN device. This paper aims to evaluate the possibility of using SUDOSCANs in chronic kidney disease prediction in diabetic patients and to investigate the relationships between clinical characteristics and SUDOSCAN parameters. The number of patients with type 2 diabetes included in this study was 254. Clinical metabolic characteristics like glycated hemoglobin, total and LDL cholesterol, triglyceride, blood pressure, and creatinine were determined along with body mass index, diabetes duration, and age. The estimated glomerular filtration rate (EGFR) was calculated and patients were grouped into three CKD stages based on EGFR values. Electrochemical skin conductance in hands and feet was determined with a SUDOSCAN device. The results showed that patients with symptomatic CKD (S2 and 3) presented lower ESC values, along with lower EFGRs and higher creatinine levels. A significant positive but weak correlation (p &lt; 0.05) was observed between SUDOSCAN nephropathy risk and EGFR. The general linear model indicated that the SUDOSCAN nephropathy risk score could be used in CKD diagnosis only if considering age, diabetes duration, and body mass index. The area under the curve (AUC) of the receiver operating characteristic (ROC) analysis revealed the moderate possibility of using the SUDOSCAN nephropathy risk score to predict CKD, since it was 0.61 (p &lt; 0.01, 95% CI 0.54\u20130.68), but only if the other factors mentioned above are included. Based on the cut-off value of 59.50 identified, patients were grouped (values above and below cut-off), and the results showed that patients with a SUDOSCAN nephropathy risk score of &lt;59.50 have lower SUDOSCAN-ESC values measured in their hands and feet, lower EGFR and higher creatinine levels. These results indicated the possibility of using SUDOSCAN as a supporting tool to identify CKD if it is correlated with other factors like age, diabetes duration, and body mass index. This is important for medical progress regarding the use of novel non-invasive technologies in identifying CKD associated with type 2 diabetes.<\/jats:p>","DOI":"10.3390\/bios15090620","type":"journal-article","created":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T09:32:32Z","timestamp":1758187952000},"page":"620","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Possible Use of the SUDOSCAN Nephropathy Risk Score in Chronic Kidney Disease Diagnosis: Application in Patients with Type 2 Diabetes"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-2795-8491","authenticated-orcid":false,"given":"Claudiu","family":"Cobuz","sequence":"first","affiliation":[{"name":"Faculty of Medicine and Biological Sciences, Stefan cel Mare University of Suceava, 13th Universitatii Street, 720229 Suceava, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1314-5957","authenticated-orcid":false,"given":"M\u0103d\u0103lina","family":"Ungureanu-Iuga","sequence":"additional","affiliation":[{"name":"Integrated Center for Research, Development and Innovation in Advanced Materials, Nanotechnologies, and Distributed Systems for Fabrication and Control (MANSiD), Stefan cel Mare University of Suceava, 13th Universitatii Street, 720229 Suceava, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8657-378X","authenticated-orcid":false,"given":"Dana-Teodora","family":"Anton-Paduraru","sequence":"additional","affiliation":[{"name":"Department of Maternal and Child Medicine, \u201cGrigore T. Popa\u201d University of Medicine and Pharmacy, 16th Universitatii Street, 700115 Ia\u015fi, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8343-5025","authenticated-orcid":false,"given":"Maricela","family":"Cobuz","sequence":"additional","affiliation":[{"name":"Faculty of Medicine and Biological Sciences, Stefan cel Mare University of Suceava, 13th Universitatii Street, 720229 Suceava, Romania"},{"name":"\u201cSf\u00e2ntul Ioan cel Nou\u201d Emergency Clinical Hospital, 720224 Suceava, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,18]]},"reference":[{"key":"ref_1","unstructured":"(2025, July 09). International Diabetes Federation About Diabetes. Available online: https:\/\/idf.org\/about-diabetes\/diabetes-facts-figures\/?utm_source=chatgpt.com."},{"key":"ref_2","unstructured":"International Diabetes Federation Romania (2025, July 09). 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