Detection of chronic obstructive pulmonary disease based on SERS and multivariate analysis of human serum

Abstract

Chronic obstructive pulmonary disease (COPD) is a significant public health disease, affecting millions of subjects globally. This study proposes a Surface-Enhanced Raman Scattering (SERS) technique to determine the presence of respiratory diseases and particularly COPD. The samples of human serum of 41 patients with respiratory diseases (11 COPD patients, 20 patients with bronchial asthma and 10 patients with asthma-COPD overlap syndrome), 103 patients with chronic heart diseases (CHD), and 25 healthy control subjects were analyzed by means of SERS. Multivariate analysis of human serum SERS characteristics was performed with Partial Least Squares Discriminant Analysis (PLS-DA) to classify (1) all respiratory diseases versus control group including CHD and healthy subjects and (2) COPD versus bronchial asthma (BA). We found that combination of SERS characteristics at 638 and 1051 cm−1 can help identifying the respiratory diseases. The PLS-DA model achieved a mean predictive accuracy of 0.92 for classifying respiratory diseases and comparable controls (0.88 sensitivity, 0.96 specificity). However, in cases of differentiation of COPD and BA a mean predictive accuracy was only 0.61. Therefore, the metabolic and proteomic composition of human serum has significant differences in the respiratory diseases group compared to the control cases, but the differences between patients with COPD and BA are less significant, indicating the similarity of serum and the general pathogenetic mechanisms of these two conditions.

Speaker

Yulia Khristoforova
Samara National Research University
Russian Federation

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