Central sensitivity syndromes (CSS), including Fibromyalgia (FM), are common and difficult to treat disorders. The diagnosis of FM depends on the presence of chronic widespread pain along with concurrent central sensitivity symptoms and absence of an alternative diagnosis. The gold standard for identification of this neurologic disorder is positive affirmation of diagnosis by a rheumatologist. FM makes up a high percentage of chronic non-malignant pain conditions which are a major burden on U.S. health care resources. In large chronic pain cohorts, over 40% meet ACR criteria for FM. Thus, individuals affected with FM and other CSS make up a substantial proportion of the population receiving opioids. Accurate identification of subjects with FM and other CSS is urgently needed in order to avoid inappropriate administration of opioids and the danger of chronic opioid treatment in at risk populations. Our proposal is innovative in both its theoretical underpinnings and methodological approach of examining spectroscopic analyses as a diagnostic adjunct in well characterized FM patients while utilizing both subjective and objective monitoring. These proposed studies can lead to evidence based alternative therapy for this population. The scientific premise is based on the knowledge that non-targeted fingerprinting approaches have been shown to have a role in identifying FM patients relative to other conditions including Rheumatoid Arthritis (RA), Osteoarthritis (OA), Systemic Lupus Erythematosus and normal controls (NC). We now seek to determine the extent to which vibrational (infrared and Raman) spectroscopy technology and supervised pattern recognition analysis (?chemometrics?) can be honed to further differentiate FM subsets and pair this approach with complementary metabolomics by LC-MS/MS to identify pharmacologic targets of interest for this condition that suffers from a lack of reliable therapeutic options. During the R61 Phase we will (Aim 1): Determine the clinical reliability of bloodspot-based biomarkers to differentiate subjects with FM from individuals with OA, CLBP (Chronic Low Back Pain), RA, SLE and NCs by vibrational spectroscopy techniques. We will use multiple validated assessment instruments to grade disease severity. (R61 Aim 2): Determine the robustness of bloodspot-based biomarker. (R61 Aim 2b): Investigate the effect of intra- and inter-assay variability, and storage conditions on biomarker reliability using known independent (not used in developing the calibration model) data sets of subjects from all groups. (R61 Aim 3): Evaluate the metabolic profile of the biological samples from our subjects. Chemometric analysis of the unique spectral patterns permits determination of bands most responsible for differentiating test subjects. During the R33 Phase:
(Aim 4) : Distinguish FM activity (flares) relative to quiescent phases of FM. Determine changes in disease activity in patients between baseline and subsequent follow up visits of each patient. The culmination of these aims will clarify the diagnosis of FM, the most frequent neuropathic disorder encountered in clinical medicine, will help alleviate unnecessary medical testing and will serve as a deterrent to opioid prescribing in this at risk population.
Fibromyalgia (FM) is a chronic non-malignant pain disorder that results from abnormalities in central pain processing and accounts for a major percentage of opioid prescriptions thus contributing to the opioid epidemic. An objective marker identifying FM could confirm the presence of disease and mitigate opiate prescribing for this condition that does not respond to them. We will evaluate the potential of vibrational (infrared and Raman) spectroscopy technology and supervised pattern recognition analysis (?chemometrics?) coupled with complementary metabolomics analyses by LC-MS/MS to differentiate FM subjects from other conditions and identify potential pharmacologic targets of interest for this condition that suffers from a lack of reliable therapeutic options.
