Data from: Layer-specific carotid ultrasound texture analysis as a supportive tool for nurse-led cardiovascular risk stratification in type 2 diabetes: a pilot study

This dataset accompanies the pilot study "Layer-specific carotid ultrasound texture analysis as a supportive tool for nurse-led cardiovascular risk stratification in type 2 diabetes mellitus". It contains anonymized data from 20 European adults (aged 40–69 years, without known cardiovascular disease): 10 participants with type 2 diabetes mellitus (T2DM) and 10 normoglycemic controls. The data include: Participant-level metadata: age, sex, BMI, SCORE2/SCORE2-Diabetes 10-year cardiovascular risk classification (low/high), and group (T2DM vs. control). Ultrasound-derived measurements: intima-media thickness (IMT) of the common carotid artery (CCA), and gray-level co-occurrence matrix (GLCM) texture features (contrast, entropy, homogeneity, correlation, etc.) extracted from four wall layers (intima-media, media, adventitia, total wall). High-resolution CCA ultrasound images were acquired under double-blind conditions. Features were extracted using standardized protocols, with participants classified by SCORE2/SCORE2-Diabetes scores. Results showed higher IMT and lower adventitial entropy in T2DM, with intima-media texture features outperforming IMT (AUC 0.94 vs. 0.87). This dataset supports reproducibility of the findings, further texture analysis in vascular ultrasound, machine learning models for cardiovascular risk biomarkers, and research on nurse-led preventive care in diabetes. It is exploratory (pilot) and requires validation in larger cohorts.