Early Diagnosis of Fibromyalgia Using Surface-Enhanced Raman Spectroscopy Combined with Chemometrics.

Area of Science:

• Biomedical Spectroscopy
• Diagnostic Technologies
• Rheumatology

Background:

• Fibromyalgia (FM) is a chronic pain disorder with overlapping symptoms with other rheumatologic conditions.
• Accurate diagnosis of FM and related diseases like rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), osteoarthritis (OA), and chronic low back pain (CLBP) remains a clinical challenge.
• Novel diagnostic tools are needed for precise differentiation of these complex conditions.

Purpose of the Study:

• To investigate the feasibility of using gold nanoparticle-enhanced Raman spectroscopy (AuNP SERS) as a diagnostic fingerprinting method for FM.
• To evaluate the potential of AuNP SERS in differentiating FM from other rheumatic diseases.
• To identify potential spectral biomarkers for label-free diagnosis of FM.

Main Methods:

• Blood samples were collected from FM patients, non-FM subjects, and healthy controls.
• A semi-permeable membrane filtration method was employed to isolate the low-molecular-weight fraction (LMF) of serum.
• Standardized AuNP SERS measurements were performed on LMF, followed by OPLS-DA analysis of spectral data (750-1720 cm⁻¹).

Main Results:

• The OPLS-DA model achieved excellent classification of spectra into FM and non-FM groups with Rcv > 0.99.
• The method demonstrated 100% accuracy, sensitivity, and specificity in differentiating the groups.
• Spectral regions associated with amino acids were identified as key discriminators, suggesting their potential as biomarkers.

Conclusions:

• The AuNP SERS method, combined with OPLS-DA analysis, is a highly accurate and feasible approach for the label-free diagnosis of FM.
• This technique shows significant potential for differentiating FM from other rheumatic conditions.
• Further research into amino acid-related spectral biomarkers could enhance diagnostic capabilities for rheumatic diseases.