Blood Chemical ‘Fingerprint’ Analysis May Help Diagnose Fibromyalgia

Blood Chemical ‘Fingerprint’ Analysis May Help Diagnose Fibromyalgia
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Detailed profiling of the biological content of blood samples can help diagnose fibromyalgia (FM) and differentiate it from other diseases such as rheumatoid arthritis or lupus, a study suggests.

The new analysis method was described by a team led by researchers from Ohio State University and uses the energy vibrational pattern of each biological molecule to identify FM-specific biomarkers.

“Advantages of such a methodology, if developed and honed to reproducibility, would be a capability for identifying specific treatment subsets for FM as well as identifying new targets as differentiated from each other metabolically,” researchers stated.

This experimental blood test was described in the study “Metabolic fingerprinting for diagnosis of fibromyalgia and other rheumatologic disorders,” published in the Journal of Biological Chemistry.

FM is a complex disorder mainly characterized by chronic widespread pain. However, most patients often report other symptoms such as fatigue, sleep problems, cognitive impairments, and affective symptoms, which can overlap with other medical conditions.

The underlying mechanisms involved in FM development are still poorly understood, but studies have demonstrated that environmental and clinical factors may contribute. This makes FM diagnosis a challenge for physicians.

Vibrational spectroscopy technology is being developed for routine use in many medical fields, including cancer, urology, and rheumatology. This technology allows rapid detailed component analysis of complex biological samples, producing a unique characteristic chemical profile for each sample.

Researchers have now tested the potential of using vibrational spectroscopy to assess FM’s characteristic “fingerprint.”

They analyzed blood samples collected from 50 patients diagnosed with FM, and compared them to those of 29 patients with rheumatoid arthritis, 23 with systemic lupus erythematosus (SLE), and 19 patients with osteoarthritis (OA).

Upon assessment of the samples through vibrational spectroscopy and broad computer analysis of the gathered information, the team could define major common components between the samples and generate a robust biomarker model.

Using this model, they could distinguish with 100% efficiency the different samples according to their corresponding disease.

“We found clear, reproducible metabolic patterns in the blood of dozens of patients with fibromyalgia. This brings us much closer to a blood test than we have ever been,” Kevin Hackshaw, associate professor at Ohio State’s College of Medicine and lead author of the study, said in a university news release.

Further analysis of 20 blood samples collected from FM patients revealed that their chemical profile, obtained by vibrational spectroscopy, correlated with self-reported disease activity (FIQR) scores.

“Vibrational spectroscopy may provide a reliable diagnostic test for differentiating FM from other disorders and for establishing serologic biomarkers of FM-associated pain,” researchers stated.

Combining chemical analysis with vibrational spectroscopy data revealed that protein backbones and pyridine-carboxylic acids were two main component groups that could help distinguish the different disease samples and identify FM-associated biomarkers.

“We can look back into some of these fingerprints and potentially identify some of the chemicals associated with the differences we are seeing,” said Hackshaw, who is also a rheumatologist at the university’s Wexner Medical Center. “This could lead to better, more directed treatment for patients.”

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