We show for the first time that the majority of fibromyalgia patients have abnormal C nociceptors. Many silent nociceptors exhibit hyperexcitability resembling that in small-fiber neuropathy, but high activity-dependent slowing of conduction velocity is more common in fibromyalgia patients, and may constitute a distinguishing feature. We infer that abnormal peripheral C nociceptor ongoing activity and increased mechanical sensitivity could contribute to the pain and tenderness suffered by patients with fibromyalgia.

BackgroundThe Revised version of the Fibromyalgia Impact Questionnaire (FIQR) was published in 2009. The aim of this study was to prepare a Spanish version, and to assess its psychometric properties in a sample of patients with fibromyalgia.MethodsThe FIQR was translated into Spanish and administered, along with the FIQ, the Hospital Anxiety Depression Scale (HADS), the 36-Item Short-Form Health Survey (SF-36), and the Brief Pain Inventory (BPI), to 113 Spanish fibromyalgia patients. The administration of the Spanish FIQR was repeated a week later.ResultsThe Spanish FIQR had high internal consistency (Cronbach’s α was 0.91 and 0.95 at visits 1 and 2 respectively). The test-retest reliability was good for the FIQR total score and its function and symptoms domains (intraclass correlation coefficient (ICC > 0.70), but modest for the overall impact domain (ICC = 0.51). Statistically significant correlations (p < 0.05) were also found between the FIQR and the FIQ scores, as well as between the FIQR scores and the remaining scales’ scores.ConclusionsThe Spanish version of the FIQR has a good internal consistency and our findings support its validity for assessing fibromyalgia patients. It might be a valid instrument to apply in clinical and investigational grounds.

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The main objective of this study is to identify fibromyalgia syndrome (FMS) clusters using the Revised Fibromyalgia Impact Questionnaire (FIQR), and to examine whether the clusters differ in sociodemographic characteristics, clinical measures, direct and indirect costs, levels of inflammatory markers, and brain morphometry. A hierarchical cluster analysis was performed to classify a large, pooled Spanish sample of patients with FMS (N = 947) using the FIQR as clustering variable. A latent profile analysis was subsequently conducted to confirm the optimal number of FMS clusters. To examine external validity, a battery of clinical measures, economic costs, inflammatory markers, and gray matter volumes of relevant cortical and subcortical areas were analyzed. We also compared the discriminant validity of the clusters with the original FIQR severity categories. To promote the implementation in real-world clinical practice, we built a free online cluster calculator. Our findings indicated that a four-cluster solution more clearly captured the heterogeneity of FIQR data and provided the best fit. This cluster solution allowed for detection of differences for most clinical outcomes and economic costs. Regarding the inflammatory and brain-based biomarkers, differences were found in C-reactive protein, and tendencies were found in the right medial prefrontal cortex, the right parahippocampal gyrus, and the right middle cingulate cortex; brain regions associated with executive functions and pain processing. The original FIQR categories presented similar results, although their precision in discriminating among the nonextreme categories (ie, moderate and severe) was not sound. These findings are discussed in relation to previous research on FMS clustering.