Metabolic Fingerprinting for the Diagnosis of Clinically Similar Long COVID and Fibromyalgia ..., 2023, Hackshaw et al.

Metabolic Fingerprinting for the Diagnosis of Clinically Similar Long COVID and Fibromyalgia Using a Portable FT-MIR Spectroscopic Combined with Chemometrics
Hackshaw, Kevin V.; Yao, Siyu; Bao, Haona; de Lamo Castellvi, Silvia; Aziz, Rija; Nuguri, Shreya Madhav; Yu, Lianbo; Osuna-Diaz, Michelle M.; Brode, W. Michael; Sebastian, Katherine R.; Giusti, M. Monica; Rodriguez-Saona, Luis

Post Acute Sequelae of SARS-CoV-2 infection (PASC or Long COVID) is characterized by lingering symptomatology post-initial COVID-19 illness that is often debilitating. It is seen in up to 30–40% of individuals post-infection. Patients with Long COVID (LC) suffer from dysautonomia, malaise, fatigue, and pain, amongst a multitude of other symptoms. Fibromyalgia (FM) is a chronic musculoskeletal pain disorder that often leads to functional disability and severe impairment of quality of life. LC and FM share several clinical features, including pain that often makes them indistinguishable.

The aim of this study is to develop a metabolic fingerprinting approach using portable Fourier-transform mid-infrared (FT-MIR) spectroscopic techniques to diagnose clinically similar LC and FM.

Blood samples were obtained from LC (n = 50) and FM (n = 50) patients and stored on conventional bloodspot protein saver cards. A semi-permeable membrane filtration approach was used to extract the blood samples, and spectral data were collected using a portable FT-MIR spectrometer.

Through the deconvolution analysis of the spectral data, a distinct spectral marker at 1565 cm−1 was identified based on a statistically significant analysis, only present in FM patients. This IR band has been linked to the presence of side chains of glutamate. An OPLS-DA algorithm created using the spectral region 1500 to 1700 cm−1 enabled the classification of the spectra into their corresponding classes (Rcv > 0.96) with 100% accuracy and specificity.

This high-throughput approach allows unique metabolic signatures associated with LC and FM to be identified, allowing these conditions to be distinguished and implemented for in-clinic diagnostics, which is crucial to guide future therapeutic approaches.

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We currently have two other papers which I've tagged with metabolic fingerprinting.

Metabolic fingerprinting reveals extensive consequences of GLS hyperactivity (2020, Biochimica et Biophysica Acta (BBA) - General Subjects)

Metabolic fingerprinting for diagnosis of fibromyalgia and other rheumatologic disorders (2019, Journal of Biological Chemistry)

 

Looking at the PubMed citations for this 2023 paper, the team have a recent related paper —

Early Diagnosis of Fibromyalgia Using Surface-Enhanced Raman Spectroscopy Combined with Chemometrics (2024)
Bao, Haona; Hackshaw, Kevin V.; Castellvi, Silvia de Lamo; Wu, Yalan; Gonzalez, Celeste Matos; Nuguri, Shreya Madhav; Yao, Siyu; Goetzman, Chelsea M.; Schultz, Zachary D.; Yu, Lianbo; Aziz, Rija; Osuna-Diaz, Michelle M.; Sebastian, Katherine R.; Giusti, Monica M.; Rodriguez-Saona, Luis

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.

Link | PDF (Biomedicines)

 

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