Work

An integrative analysis of cardiac autonomic neuropathy and nephropathy risk assessed with SUDOSCAN in individuals with type 2 diabetes

Year: 2026

Type: article

Abstract: Introduction The use of non-invasive, rapid screening methods to detect diabetes mellitus complications, such as neuropathy, is a growing trend in modern medicine. This study aimed to investigate the relationship between SUDOSCAN-derived Cardiac Autonomic Neuropathy (CAN) and Nephropathy (Nephro) scores in individuals with type 2 diabetes mellitus and to evaluate the potential of artificial neural networks in predicting these scores. Methods A cross-sectional study was conducted, and 150 individuals were included in the statistical analysis to determine the risk of CAN and nephropathy in individuals with type 2 diabetes mellitus using the SUDOSCAN device. The relationships between SUDOSCAN-derived scores and covariate factors (age, sex, diabetes duration, and body mass index) were established through Spearman correlations, a general linear model, and an artificial neural network (ANN). Results The results indicated that individuals with diabetes are at higher risk of both cardiac autonomic neuropathy and nephropathy, which are strongly interconnected, mainly due to factors like age, BMI, and blood pressure rather than traditional glycemic markers. A strong inverse correlation was observed between CAN and nephropathy scores (r = -0.83, p < 0.05), highlighting a shared mechanism such as endothelial dysfunction and metabolic stress. The CAN score model showed slightly better predictive performance (RMSE 5.36, MAE 4.11) than the nephropathy model (RMSE 5.91, MAE 7.55), while artificial neural networks achieved outstanding classification performance (AUC ≥ 0.97). Discussion When used together, the highly sensitive CAN model can be employed for initial screening to prevent missing cases, while the highly-specific Nephro model can confirm risk and minimize false positives, thereby creating an optimal two-step risk stratification strategy. Thus, ANN-based systems can assist clinicians in guiding decisions by prioritizing individuals for further testing, tailoring treatments, and optimizing follow-up care in diabetic nephropathy.

Source: Frontiers in Endocrinology

DOI: 10.3389/fendo.2026.1712085

Authors: Claudiu Cobuz, Mădălina Ungureanu-Iuga, Maricela Cobuz

Institutions: Ştefan cel Mare University of Suceava, Spitalul Clinic de Urgenta Sfantul Ioan

Language: English

Topic: Diabetes Management and Research

Subfield: Endocrinology, Diabetes and Metabolism

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,801