Spectroscopy IDs Potential Metabolic Markers to Diagnose Fibromyalgia

The identification of metabolic patterns in the blood of patients with fibromyalgia may improve diagnosis and enable discovery of targeted treatments, according to a study published Feb. 15 in the Journal of Biological Chemistry.

Fibromyalgia is part of a larger group of chronic pain syndromes (e.g., chronic fatigue syndrome and irritable bowel syndrome), but definitive diagnosis remains a challenge due to the lack of reliable biomarkers. Currently, doctors rely on patient-reported symptoms and a physical evaluation of a patient’s pain, focusing on specific tender points.

“Unfortunately, no reliable diagnostic test for fibromyalgia exists,” write the authors led by Kevin V. Hackshaw, M.D., from Ohio State University in Columbus. Such a test would be a significant step towards earlier diagnosis of and intervention for this condition, helping to improve patient outcomes, contain health care and/or legal costs, and potentially provide clues to the etiopathogenesis of the syndrome.”

The researchers used dried blood spots of peripheral blood samples (derived from fingersticks) from patients with a diagnosis of fibromyalgia (n = 50), rheumatoid arthritis (n = 29), osteoarthritis and  (n = 19), and systemic lupus erythematosus (n = 23). Bloodspot samples were analyzed using vibrational microspectroscopy (a portable FT-IR and FT-Raman microspectroscopy) and also underwent metabolomics analysis by ultra high-performance liquid chromatography (uHPLC), coupled to a photodiode array (PDA) and tandem mass spectrometry (MS/MS).

Vibrational spectroscopy measures the energy level of molecules enabling rapid, high-throughput, and non-destructive analysis. Analysis produces a characteristic chemical ‘fingerprint’ with a unique signature profile with spectra separated into discrete clusters that permit classification of individuals based on subtle physiological differences, the authors say. The patterns are based on the “predictable ways” different functional groups absorb infrared light.

The researchers report that FT-IR and Raman spectroscopies produced distinct clustering of the specimen samples according to their disease class with no misclassifications. This discrimination power, the authors say, was dominated by vibrations of the backbone in proteins and nucleic acids, in addition to mineral differences in blood. Additionally, the spectra correlated (for both IR and Raman) with fibromyalgia pain severity measured with a validated questionnaire.

From each disease group, 10 randomly selected samples were analyzed by uHPLC- PDA-MS/MS. This technology also was able to distinguish between disease groups with certain metabolites existing in significantly different proportions as witnessed on the ultraviolet-visible light chromatograms.

“We found clear, reproducible metabolic patterns in the blood of dozens of patients with fibromyalgia,” said Hackshaw. “Our studies have great importance both from development of a reproducible biomarker as well as identifying potential new therapeutic targets for treatment.”

The authors say the next phase of their research will include an expanded study of 150 to 200 patients per disease group to see if the findings can be validated in a larger, more diverse population. The hope is to have a test ready for widespread use within five years.

Takeaway: Spectroscopy technology has identified metabolic patterns from dried blood spots that may lead to an objective diagnostic test for fibromyalgia.


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