Autonomic nervous system dysfunction and fatigue in multiple sclerosis: common pathophysiology or spurious association?

Gait and heart rate: do they measure trait or state physical fatigue in people with multiple sclerosis?

BACKGROUND

Trait and state physical fatigue (trait-PF and state-PF) negatively impact many people with multiple sclerosis (pwMS) but are challenging symptoms to measure. In this observational study, we explored the role of specific gait and autonomic nervous system (ANS) measures (i.e., heart rate, HR, r-r interval, R-R, HR variability, HRV) in trait-PF and state-PF.

METHODS

Forty-eight pwMS [42 ± 1.9 years, 65% female, EDSS 2 (IQR: 0-5.5)] completed the Timed Up and Go test (simple and with dual task, TUG-DT) and the 6-min walk test (6MWT). ANS measures were measured via a POLAR H10 strap. Gait was measured using inertial-measurement units (OPALs, APDM Inc). Trait-PF was evaluated via the Modified Fatigue Impact Scale (MFIS) motor component. State-PF was evaluated via a Visual Analog Scale (VAS) scale before and after the completion of the 6MWT. Multiple linear regression models identified trait-PF and state-PF predictors.

RESULTS

Both HR and gait metrics were associated with trait-PF and state-PF. HRV at rest was associated only with state-PF. In models based on the first 3 min of the 6MWT, double support (%) and cadence explained 47% of the trait-PF variance; % change in R-R explained 43% of the state-PF variance. Models based on resting R-R and TUG-DT explained 39% of the state-PF.

DISCUSSION

These findings demonstrate that specific gait measures better capture trait-PF, while ANS metrics better capture state-PF. To capture both physical fatigue aspects, the first 3 min of the 6MWT are sufficient. Alternatively, TUG-DT and ANS rest metrics can be used for state-PF prediction in pwMS when the 6MWT is not feasible.

Unveiling the relationship between autonomic involvement, fatigue, and cognitive dysfunction in early relapsing-remitting multiple sclerosis

BACKGROUND

Fatigue is a common, yet disabling, symptom in patients with multiple sclerosis (PwMS). Fatigue has shown to be associated with self-reported autonomic nervous system (ANS) symptoms, particularly for cognitive fatigue; however, the question whether ANS involvement is related to cognitive impairment has never been addressed. We performed a study to unveil the complex relationship between fatigue, ANS symptoms, and cognitive impairment.

METHODS

We prospectively recruited early PwMS that were tested with Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS), State-Trait Anxiety Inventory (STAI), Beck Depression Inventory (BDI), Modified Fatigue Impact Scale (MFIS) and Composite Autonomic Symptoms Scale-31 (COMPASS-31) scale. We performed a comparison between fatigued and non-fatigued patients and between cognitive unimpaired and impaired patients. We evaluated the association of COMPASS-31, MFIS, BDI, STAI, and BICAMS scores, and the analysis was repeated for each scale's sub-scores. A multivariable analysis was performed to elucidate predictors of fatigue.

RESULTS

Forty-four patients were recruited. Fatigued patients had higher COMPASS-31 total, orthostatic intolerance, secretomotor, and pupillomotor scores. No differences in fatigue and ANS symptoms were found between cognitive impaired and unimpaired patients. MFIS total score correlated with STAI state (p = 0.002) and trait (p < 0.001), BDI (p < 0.001), COMPASS-31 total (p < 0.001), orthostatic intolerance (p < 0.001), pupillomotor scores (p = 0.006). Multivariable analysis showed that BDI (p < 0.001) and COMPASS-31 (p = 0.021) predicted MFIS score. Sub-scores analysis showed that orthostatic intolerance has a relevant role in fatigue.

CONCLUSION

ANS symptoms are closely related with fatigue. Orthostatic intolerance may have a predominant role. Cognitive impairment seems not to be associated with ANS symptoms.