Biomedicines (Jan 2024)

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

  • Haona Bao,
  • Kevin V. Hackshaw,
  • Silvia de Lamo Castellvi,
  • Yalan Wu,
  • Celeste Matos Gonzalez,
  • Shreya Madhav Nuguri,
  • Siyu Yao,
  • Chelsea M. Goetzman,
  • Zachary D. Schultz,
  • Lianbo Yu,
  • Rija Aziz,
  • Michelle M. Osuna-Diaz,
  • Katherine R. Sebastian,
  • Monica M. Giusti,
  • Luis Rodriguez-Saona

DOI
https://doi.org/10.3390/biomedicines12010133
Journal volume & issue
Vol. 12, no. 1
p. 133

Abstract

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Fibromyalgia (FM) is a chronic muscle pain disorder that shares several clinical features with other related rheumatologic disorders. This study investigates the feasibility of using surface-enhanced Raman spectroscopy (SERS) with gold nanoparticles (AuNPs) as a fingerprinting approach to diagnose FM and other rheumatic diseases such as rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), osteoarthritis (OA), and chronic low back pain (CLBP). Blood samples were obtained on protein saver cards from FM (n = 83), non-FM (n = 54), and healthy (NC, n = 9) subjects. A semi-permeable membrane filtration method was used to obtain low-molecular-weight fraction (LMF) serum of the blood samples. SERS measurement conditions were standardized to enhance the LMF signal. An OPLS-DA algorithm created using the spectral region 750 to 1720 cm−1 enabled the classification of the spectra into their corresponding FM and non-FM classes (Rcv > 0.99) with 100% accuracy, sensitivity, and specificity. The OPLS-DA regression plot indicated that spectral regions associated with amino acids were responsible for discrimination patterns and can be potentially used as spectral biomarkers to differentiate FM and other rheumatic diseases. This exploratory work suggests that the AuNP SERS method in combination with OPLS-DA analysis has great potential for the label-free diagnosis of FM.

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