Surface-enhanced Raman Spectroscopy of Blood Serum for Clustering Patients with Clinically Evident Atherosclerosis

Abstract

Abstract — A current focus in modern cardiology is the identification of novel circulating metabolomic and proteomic parameters that may serve as integral biomarkers of atherosclerosis and its phenotypes, including ischemic heart disease (IHD) and its combinations with chronic heart failure (CHF). Conventional analytical techniques for assessing blood biochemical parameters, such as chromatography and mass spectrometry, are labor-intensive, complex, and costly. Surface-enhanced Raman spectroscopy (SERS) emerges as a promising technique that generates a spectral portrait of disease, enabling the identification of unfavorable features predictive of atherosclerosis development and progression through mathematical modeling. This study aimed to evaluate the utility of serum SERS in classifying patients according to distinct phenotypes of peripheral atherosclerosis and IHD. Materials —The multifocal atherosclerosis (MFA) group included 69 patients of both sexes, while the IHD complicated by CHF group comprised 61 age- and sex-matched patients. The control group consisted of 75 patients without clinical signs of atherosclerosis. Results —Mathematical modeling using projection to latent structures discriminant analysis (PLS-DA) of serum SERS data demonstrated high accuracy (0.93–1.00) in distinguishing patients with clinical manifestations of atherosclerosis from those without, based on spectral frequencies of 670–680, 718, 1004, 1073, 1146, and 1439 cm-1. The SERS method enables the detection of a metabolic blood profile associated with clinically manifest CHF syndrome (NYHA class II–III) complicating IHD at frequencies of 672, 728, 1077, 1123, 1214, 1284, and 1402 cm-1. Given the relative simplicity and high discriminative power of the SERS method, it holds promise for conducting studies aimed at detecting subclinical stages of diseases associated with atherosclerosis, refining IHD risk stratification, and optimizing therapy for affected patients. Summary — Significant differences detected by SERS enable reliable classification of patient group membership among the three studied groups.