Cross‐sectional and longitudinal analysis of conditioned pain modulation and pain in fibromyalgia: CPM as an effect modifier of pain changes over time

Abstract

Background and PurposeFibromyalgia (FM) is associated with altered descending pain modulatory pathways, which can be assessed through Conditioned Pain Modulation (CPM). In this study, we aimed to explore the relationship between CPM and self‐reported baseline characteristics in patients with fibromyalgia. We also performed a longitudinal analysis exploring CPM as a potential predictor of clinical improvement over time in individuals with FM.MethodsWe performed cross‐sectional univariable and multivariable analyses of the relationship between CPM and other variables in 41 FM patients. We then performed longitudinal analyses, building linear mixed‐effects models with pain in the Visual Analogue Scale (VAS) as the dependent variable, and testing for the interaction between time and CPM. We also tested the interaction between CPM and time in models using other outcomes, such as the revised Fibromyalgia Impact Questionnaire and Quality of Life Scale (QOLs).ResultsWe found no association between CPM and other demographic and clinical variables in the univariable analysis. We found a statistically significant association in the multivariable linear regression model between CPM and the QOLs at the baseline after controlling for age, sex, and duration of symptoms. In the longitudinal analyses, we found that CPM is an effect modifier for clinical improvement over time for the pain VAS, QOLs, and FIQR: individuals with low‐efficient CPM at the baseline have a different (improved) pattern of response over time when compared to those with high‐efficient CPM.ConclusionsOur findings suggest that CPM is not a reliable biomarker of clinical manifestations in chronic pain patients during cross‐sectional assessments. However, our results are consistent with previous findings that CPM can be used to predict the evolution of clinical pain over time. We expect that our findings will help in the selection of patients with the best profile to respond to specific interventions and assist clinicians in tailoring pain treatments.