A noninvasive method based on surface-enhanced Raman spectroscopy (SERS) of tears was proposed as a support for diagnosing neurodegenerative pathologies, including different forms of dementia and Alzheimer’s disease (AD). In this field, timely and reliable discrimination and diagnosis are critical aspects for choosing a valid medical therapy, and new methods are highly required.
The aim is to evince spectral differences in SERS response of human tears from AD affected, mild cognitive impaired (MCI), and healthy control (Ctr) subjects.
Human tears were characterized by SERS coupled with multivariate data analysis. Thirty-one informed subjects (Ctr, MCI, and AD) were considered.
Average SERS spectra from Ctr, MCI, and AD subjects evidenced differences related to lactoferrin and lysozyme protein components. Quantitative changes were also observed by determining the intensity ratio between selected bands. We also constructed a classification model that discriminated among AD, MCI, and Ctr subjects. The model was built using the scores obtained by performing principal component analysis on specific spectral regions (i-PCA).
The results are very encouraging with interesting perspectives for medical applications as support of clinical diagnosis and discrimination of AD from other forms of dementia.