Recently, we have developed a novel methodology integrating centrifugal ultrafiltration and Attenuated Total Reflectance-Fourier transform infrared (ATR-FTIR) . The technique relies on machine learning (ML) methodologies to extract the information about the presence and abundance of specific proteins in the sample associated to DKD. Indeed, the use of sophisticated ML techniques to create predictive models is a common approach in clinical vibrational spectroscopy, where infrared (IR) and Raman spectra are treated with classification and regression approaches to offer label-free, fast, and costeffective alternatives to label-based techniques. As an example, diseases like cancer , malaria , and COVID-19 have been targeted for diagnosis using vibrational spectroscopy techniques, and these methods have demonstrated potential in identifying spectral markers, facilitating early detection and diagnosis.