Fatigue and Depression Common in Early Multiple Sclerosis

The study covered in this summary was published in medRxiv.org as a preprint and has not yet been peer reviewed.

Key Takeaways

• This study found robust links between subjective fatigue and depression in early relapsing-remitting multiple sclerosis (RRMS), despite the lack of associations between fatigue and either objective cognitive performance or structural brain imaging variables.

• Depression, including specific depressive symptoms, could be a major target of treatment and research in multiple sclerosis–related fatigue.

Why This Matters

• MS is an autoimmune-mediated neuroinflammatory and neurodegenerative condition. It is a leading cause of morbidity in young adults and affects more than two million people globally.

• Despite the high prevalence of disabling fatigue in individuals with MS, its mechanisms are unclear. Compounding the difficulty in interpretation is the overlap in measures of fatigue and depression.

  Recommended:

  ---

  • Computer program that uses data from wearable technology to detect depression
  • Best way to boost the immune system after a single Janssen vaccination is with mRNA booster

  ---

• It can be challenging to distinguish subjective fatigue from physical fatigability, depression, tiredness, and other phenomena that are often present in people with MS.

• Despite the high frequency of fatigue, treatment options in individuals with MS are of limited success.

Study Design

• Participants were obtained from FutureMS, a nationally representative cohort of patients with newly diagnosed RRMS in Scotland. This cohort accounted for an estimated 45% of persons diagnosed with RRMS in Scotland during the study period, with data gathered at baseline and at 12 months’ follow-up.

• All participants were evaluated according to a wide range of clinical measures and structural brain MRI data.

• Investigators used the validated Fatigue Severity Scale to assess subjective fatigue.

• They performed detailed phenotyping, which consisted of measures evaluating physical disability, affective disorders, objective cognitive performance, and subjective sleep quality.

• They calculated bivariate correlations between fatigue and other variables.

• They conducted network analysis to estimate partial correlations between variables after controlling for all other included variables. Secondary networks included individual depressive symptoms, to account for overlapping symptom items in measures of fatigue and depression.

Key Results

• The study included data from 322 participants at baseline, at which point 49.5% of the cohort had clinically significant fatigue.

• Bivariate correlations proved that fatigue severity corresponded significantly with all included measures of physical disability, affective disturbance (anxiety and depression), cognitive performance (processing speed and memory/attention), and sleep quality, but not with structural brain imaging variables, including normalized lesion and gray matter volumes.

• In the network analysis, fatigue revealed strong correlations with depression, followed by Expanded Disability Status Scale.

• There were weak connections with walking speed, subjective sleep quality, and anxiety.

• After separately accounting for measurement of “tiredness” in the authors’ evaluation of depression, some key depressive symptoms (anhedonia, subjective concentration deficits, subjectively altered speed of movement, and appetite) were still linked to fatigue.

• On the other hand, fatigue was not correlated with objective cognitive performance, white matter lesion volume, or gray matter volumes (cortical, subcortical, or thalamic).

• Results were steady at baseline and month 12.

• Depression was noted as the most central variable in the networks.

• Correlation stability coefficients and bootstrapped confidence intervals of the edge weights upheld stability of the estimated networks.

Limitations

• The authors measured fatigue severity using FSS, which, despite validation for use in people with MS, may concentrate preferentially on physical aspects of fatigue.

• Causality cannot be derived from these cross-sectional analyses.

• Future studies, including data collection at multiple time points to establish longitudinal networks, are necessary.

• The authors could not factor in all variables that may be associated with fatigue. For example, pain has been found to closely correlate with both fatigue and depression.

Study Disclosures

• The authors report no conflicts of interest.

This is a summary of a preprint research study, “Data-Driven Analysis Shows Robust Links Between Fatigue and Depression in Early Multiple Sclerosis,” by Yuan-Ting Chang and colleagues from University of Edinburgh, Edinburgh, United Kingdom, provided to you by Medscape. This study has not yet been peer reviewed. The full text of the study can be found on medRxiv.org.

For more news, follow Medscape on Facebook, Twitter, Instagram, and YouTube.

Source: Read Full Article