Research on the difference between patients with coronary heart disease and healthy controls by surface enhanced...

Area of Science:

• Biomedical Engineering
• Spectroscopy
• Cardiovascular Disease Diagnostics

Background:

• Coronary heart disease (CHD) is a leading global cause of mortality.
• Current diagnostic methods for CHD are often invasive and lack optimal accuracy.
• There is a critical need for non-invasive, accurate diagnostic tools for CHD.

Purpose of the Study:

• To investigate the potential of surface-enhanced Raman spectroscopy (SERS) for non-invasive CHD diagnosis.
• To analyze urine samples from CHD patients and healthy controls using SERS.
• To develop and validate a predictive model for CHD classification based on SERS data.

Main Methods:

• Collected urine samples from 157 CHD patients and 63 healthy controls (HC).
• Performed SERS measurements on all urine samples.
• Utilized statistical analysis, including principal component analysis (PCA) and linear discriminant analysis (LDA), for data interpretation and model development.
• Validated the prediction model using leave-one-patient-out cross-validation (LOPOCV).

Main Results:

• Identified significant intensity differences in nine specific Raman peaks between CHD patients and HC.
• The developed PCA-LDA model achieved a diagnostic accuracy of 84.09%.
• The model demonstrated high specificity (92.06%) and sensitivity (80.89%) in distinguishing between CHD and HC.

Conclusions:

• Surface-enhanced Raman spectroscopy (SERS) shows promise as a non-invasive diagnostic tool for CHD.
• The proposed SERS-based method is rapid, accurate, and suitable for clinical applications.
• This technique offers a valuable alternative to existing invasive diagnostic procedures for coronary heart disease.