Personalized Biomarkers and Neuropsychological Status Can Predict Post-Stroke Fatigue

Thyroid hormones and stroke, the gap between clinical and experimental studies

Despite plenty of human studies on changes in thyroid hormones after stroke and some animal studies that assessed the effects of thyroid hormone administration on stroke, conclusive evidence for clinical application is lacking. This review aimed to determine the consistency of the results between clinical and preclinical studies. This article reviewed the PubMed, Embase, web of Knowledge, and Google Scholar databases up to June 2023 using the MeSH terms "stroke, cerebral ischemia, cerebral infarction, brain ischemia, brain infarction, triiodothyronine (T3), tetraiodothyronine (T4), thyroxine (T4), and thyroid hormone". The results of clinical and preclinical studies related to T3 substantially confirm each other. That is, in most human studies lower T3 was associated with poor outcomes, and in experimental studies, T3 administration also had therapeutic effects. However, the results of experimental studies related to T4 could not support those of clinical studies. There seem to be some conflicts between experimental and human studies, especially regarding changes and effects of T4 after stroke. The gap between experimental and clinical studies may lead to non-applicable results, wasting time and money, and unnecessary killing of animals.

Post-stroke fatigue: a review of development, prevalence, predisposing factors, measurements, and treatments

Background

Post-stroke fatigue (PSF) is a ubiquitous and overwhelming symptom for most stroke survivors. However, there are no effective management strategies for PSF, which is partly due to our limited understanding.

Objective

In this paper, we review the development, prevalence, predisposing factors, measurements, and treatments of PSF.

Results

PSF is an independent symptom after stroke, with a prevalence ranging from 42 to 53%, which depends on the selection of measurement tools and stroke characteristics. It is affected by biological, physical, and psychological factors, among which inflammation may play a key role.

Conclusion

Numerous but non-specific evaluation measurement tools limit the management of PSF. In clinical practice, it may be beneficial to identify PSF by combining scales and objective indexes, such as walking tests and electromyographic examinations. There are no evidence-based interventions to improve PSF. However, increasing evidence suggests that transcranial direct-current stimulation and mindfulness-based interventions may become promising treatments. Further studies are urgently needed to better understand the etiology of PSF, thereby providing the basis for developing new measurement tools and targeted treatments.

Neuroimaging correlates of post-stroke fatigue: A systematic review and meta-analysis

BACKGROUND

Fatigue is a common and disabling symptom following stroke, but its underlying mechanisms are unknown. Associations with a number of imaging features have been proposed.

AIMS

We aimed to assess whether neuroimaging parameters could better inform our understanding of possible causes of post-stroke fatigue (PSF) through systematic review and meta-analysis.

METHODS

Using a predefined protocol registered with PROSPERO (ID: CRD42022303168), we searched EMBASE, MEDLINE, PubMed, and PsycInfo for studies assessing PSF and computerized tomography (CT), magnetic resonance (MR), positron emission tomography (PET) imaging, or diffusion tensor imaging (DTI). We extracted neuroimaging parameters and narratively analyzed study results to assess any association with PSF. Where there were 3+ similar studies, we carried out a meta-analysis using inverse-variance random-effects model to estimate the total association of each neuroimaging parameter on PSF. The risk of bias was assessed using the Newcastle and Ottawa Scale.

RESULTS

We identified 46 studies (N = 6543); in many studies, associations with fatigue were secondary or subanalyses (28.3%). Imaging parameters were assessed across eight variables: lesion lateralization, lesion location, lesion volume, brain atrophy, infarct number, cerebral microbleeds, white matter hyperintensities (WMHs), and network measures. Most variables showed no conclusive evidence for any association with fatigue. Meta-analysis, where possible, showed no association of the following with PSF; left lesion lateralization (OR: 0.88, 95% CI (0.64, 1. 22) (p = 0.45)), infratentorial lesion location (OR: 1.83, 95% CI (0.63, 5.32) (p = 0.27)), and WMH (OR: 1.21, 95% CI (0.84, 1.75) (p = 0.29)). Many studies assessed lesion location with mixed findings; only one used voxel-symptom lesion-mapping (VSLM). Some small studies suggested an association between altered functional brain networks, namely frontal, fronto-striato-thalamic, and sensory processing networks, with PSF.

CONCLUSION

There was little evidence for the association between any neuroimaging parameters and PSF. Future studies should utilize advanced imaging techniques to fully understand the role of lesion location in PSF, while the role of altered brain networks in mediating PSF merits further research.