ABSTRACT
BACKGROUND: The different clinical presentations of fibromyalgia (FMS) may play independent roles in the unclear etiology of cognitive impairments and depressive symptoms seen in this population. Understanding how these clinical presentations are associated with FMS's clinical and neurophysiological aspects is important when developing effective treatments. AIM: To explore the relationship between memory complaints and depressive symptoms, and the different clinical and neurophysiological characteristics of FMS. METHODS: Cross-sectional data analysis from a randomized clinical trial. Baseline demographics, physical fitness, sleep, anxiety, depression, cortical excitability, and pain (clinical and mechanistic) data from 63 FMS subjects were used. Multiple linear and logistic association models were constructed. RESULTS: Final regression models including different sets of predictions were statistically significant (p < 0.001), explaining approximately 50% of the variability in cognitive complaints and depression status. Older subjects had higher levels of anxiety, poor sleep quality, lower motor threshold, and higher relative theta power in the central area, are more likely to have clinical depression. Higher anxiety, pain and theta power were associated with an increase memory complaint. CONCLUSION: Depression symptoms seem to be associated with TMS-indexed motor threshold and psychosocial variables, while memory complaints are associated with pain intensity and higher theta oscillations. These mechanisms may be catalyzed and/or triggered by some behavioral and clinical features such as older age, sleep disruption, and anxiety. The correlation with clinical variables suggests the increasing of theta oscillations is a compensatory response in patients with FMS, which can be explored in future studies to improve the treatment for FMS.
ABSTRACT
BACKGROUND: Clinical predictors of sleep quality in patients with fibromyalgia syndrome (FMS) are still unknown. By identifying these factors, we could raise new mechanistic hypotheses and guide management approaches. We aimed to describe the sleep quality of FMS patients and to explore the clinical and quantitative sensory testing (QST) predictors of poor sleep quality and its subcomponents. METHODS: This study is a cross-sectional analysis of an ongoing clinical trial. We performed linear regression models between sleep quality (Pittsburgh Sleep Quality Index [PSQI]) and demographic, clinical, and QST variables, controlling for age and gender. Predictors for the total PSQI score and its seven subcomponents were found using a sequential modeling approach. RESULTS: We included 65 patients. The PSQI score was 12.78 ± 4.39, with 95.39% classified as poor sleepers. Sleep disturbance, use of sleep medications, and subjective sleep quality were the worst subdomains. We found poor PSQI scores were highly associated with symptom severity (FIQR score and PROMIS fatigue), pain severity, and higher depression levels, explaining up to 31% of the variance. Fatigue and depression scores also predicted the subjective sleep quality and daytime dysfunction subcomponents. Heart rate changes (surrogate of physical conditioning) predicted the sleep disturbance subcomponent. QST variables were not associated with sleep quality or its subcomponents. CONCLUSIONS: Symptom severity, fatigue, pain, and depression (but no central sensitization) are the main predictors of poor sleep quality. Heart rate changes independently predicted the sleep disturbance subdomain (the most affected one in our sample), suggesting an essential role of physical conditioning in modulating sleep quality in FMS patients. This underscores the need for multidimensional treatments targeting depression and physical activity to improve the sleep quality of FMS patients.
