PLSDA was used to study the spectral markers associated to qualitative and quantitative changes in proteins, as the blackbox nature of SVMDA and RF poses several challenges in the interpretation of the models. PLSDA regression vectors for global classification models are shown in Figure , with points coloured according to the variable importance projection (VIP) score. While both regression vectors show similarities in the higher region (3200-2800 cm -1 ), they also display notable differences. In the DKD classification (see Figure ), the C-O and C-C stretching vibrations found at 1200-1000 cm -1 associated with glycoproteins are extremely negative . Furthermore, the VIP scores highlight the importance of these variables along with the Amide I and Amide II region. All these differences are in accordance with the normalized mean spectra of Control and DKD samples from both datasets (Fig. ), where the 3600-3200 cm -1 and 1200-1000 cm -1 regions show a higher absorbance in controls. In summary, the analysis of the discriminant features following spectral normalization for DKD indicates that patients are classified based on their specific protein profiles. In healthy individuals, low levels of proteins, primarily uromodulin, are typically excreted, and a reduced urinary proportion of this glycoprotein has been linked to kidney damage . In contrast, the presence of DKD increases considerably the proportion of albumin.