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Published in final edited form as: J Stroke Cerebrovasc Dis. 2014 Dec 23;24(3):618–621. doi: 10.1016/j.jstrokecerebrovasdis.2014.10.008

Poststroke Fatigue: Hints to a Biological Mechanism

Kyra Becker *, Ruth Kohen †, Richard Lee *, Patricia Tanzi *, Dannielle Zierath *, Kevin Cain ‡, Pamela Mitchell §, Jonathan Weinstein *
PMCID: PMC4359660  NIHMSID: NIHMS651802  PMID: 25542762

Abstract

Background

Poststroke fatigue (PSF) is common, but the biological basis of this fatigue is unknown. We explored the possibility that PSF is related to systemic inflammation by investigating polymorphisms in 2 genes that affect the immune response.

Methods

In a substudy of a larger trial that evaluated the role of the immune response on stroke outcome, fatigue was assessed at 30, 90, 180, and 365 days after ischemic stroke using the Fatigue Assessment Scale. Subjects were genotyped for 3 single nucleotide polymorphisms, one in the interleukin-1 receptor antagonist gene (IL1RN; rs4251961, a T/C substitution) and two in the in toll-like receptor-4 (TLR4) gene (1063 A/G [Asp299Gly] rs4986790 and 1363 C/T [Thr399Ile] rs4986791).

Results

Of the 39 participants, 22 (56%) endorsed fatigue during the study. The degree of fatigue was remarkably constant over time and independent of stroke outcome. The C allele of the rs4251961 single nucleotide polymorphism (SNP) in IL1RN was associated with self-reported fatigue (P = .03), whereas the cosegregating polymorphisms in TLR4 were associated with lower levels of fatigue (P = .04).

Conclusions

SNPs in 2 genes with opposing effects on inflammatory immune responses were significantly, but differentially, associated with PSF. These findings suggest a direct link between immune signaling dysregulation and PSF.

Keywords: Poststroke fatigue, inflammation, polymorphisms, IL1RN, TLR4

Poststroke fatigue (PSF) is common after stroke and adversely affects quality of life.1,2 Despite the high prevalence of PSF and its negative impact on recovery, the biological underpinnings of PSF are unknown. A potential role for inflammation in the genesis of PSF has been considered.3-5 We hypothesized that if inflammation contributes to PSF, single nucleotide polymorphisms (SNPs) in genes that affect the immune response may be associated with PSF. The SNP rs4251961 is located in the promoter region of IL1RN; its C allele is associated with lower circulating concentrations of the gene product (interleukin, IL-1ra) and higher concentrations of proinflammatory cytokines.6,7 In the toll-like receptor-4 (TLR4) gene, 2 functional polymorphisms, 1063 A/G (Asp299Gly; rs4986790) and 1363 C/T (Thr399Ile; rs4986791), are described. In Caucasians, these 2 SNPs cosegregate such that it is more common for them to occur together rather than independently.8 These TLR4 SNPs result in altered TLR4 proteins with decreased responsiveness to TLR4 ligands.8,9 Given their opposing effects on systemic inflammatory responses, we hypothesized that the IL1RN SNP and the 2 cosegregating TLR4 SNPs would be associated with different rates of PSF.

Methods

Research Subjects

The parent–patient population is described elsewhere.10 Briefly, patients with ischemic stroke admitted to Harborview Medical Center from September 2005 through May 2009 who were at least 18 years of age were enrolled within 72 hours of symptom onset. Individuals with ongoing therapy for malignancy, known history of human immunodeficiency virus, hepatitis B or C, history of brain tumor, anemia (hematocrit <35 on admission), and those taking immunomodulatory drugs were excluded. All study procedures were approved by the University of Washington Institutional Review Board.

Clinical Data

Clinical and demographic data were collected on all subjects. Stroke severity was determined by the National Institutes of Health Stroke Scale score. Outcome was assessed by the modified Rankin Scale (mRS) score. Total infarct volume on initial diffusion-weighted magnetic resonance imaging was calculated by the ABC/2 method.11 Subjects were asked about fatigue by the study nurse using the Fatigue Assessment Scale (FAS), a well characterized scale for assessing PSF.12 Approval to administer the FAS was obtained approximately 30 months after study onset. This article includes data from the 39 subjects who provided FAS data at one or more time points. Subjects were also asked if they felt sad or blue at these same time points.

Genotyping

DNA was extracted from blood plasma samples using QIAamp DNA Blood Mini Kit (Qiagen, Valencia, CA) per manufacturer's protocols. For all 3 of the SNPs examined, genotyping was carried out using TaqMan SNP Genotyping Assay Sets and Master Mix (Applied Bio-systems, Carlsbad, CA). In brief, 2 ng of sample DNA was genotyped per manufacturer's protocols on StepOne-Plus Real-Time PCR (polymerase chain reaction) System (Applied Biosystems) under the following cycling conditions: 95° C for 10 minutes, then 40 cycles of 95° C for 15 seconds, and 60° C for 1 minute. An allelic discrimination plot was then generated using StepOne Software, v2.0 (Applied Biosystems). Target SNP reference identification numbers were rs4986790 and rs4986791 for the 2 TLR4 SNPs and rs4251961 for the IL1RN SNP. All samples were processed in triplicate. Reproducibility of the geno-typing method was confirmed as described.10 In brief, plasma-based PCR genotyping method was confirmed by carrying out identical PCR-based genotyping on DNA extracted from isolated leukocytes in a subset (n = 42) of patients. In these 42 patients, there was 100% concordance between the plasma-based and leukocyte-based samples. Genotype distributions for all 3 SNPs did not differ significantly from Hardy–Weinberg equilibrium (not shown).

Statistics

Descriptive data for continuous variables are presented as mean and standard deviation or median and interquartile range and compared using t tests for normally distributed data and the Mann–Whitney U test for non–normally distributed data. Data for categorical variables are presented as percentages and compared using the linear-by-linear association. Good outcome was defined as mRS less than 2. Patients were categorized based on the highest observed FAS score using previously defined cut points: 10-21 = not fatigued, 22-34 = fatigued, and 35-50 = very fatigued.13 Significance was set at P less than .05.

Results

Individual FAS scores over time are shown in Figure 1. Median FAS scores did not differ over time and were similar among those with good outcome (mRS <2) and those without. Among our 39 participants, 17 (44%) did not endorse fatigue (FAS, 10-21) at any time point after stroke, 14 (36%) had fatigue (FAS, 22-34) at one or more time points, and 8 (20%) felt extremely fatigued (FAS, 35-50) at one or more time points in the year after stroke. The clinical characteristics of these subjects are shown in Table 1. In this cohort, there was no relationship between fatigue and infarct volume, infarct location, or infarct etiology (as determined by the Trial of Org 10172 in Acute Stroke Treatment criteria14). Among subjects who endorsed feeling sad at one or more time points during the study period, 12 of 15 (80%) had at least one FAS score of 22 or more compared with 10 of 24 (40%) of patients who consistently denied feeling sad (P = .016).

Figure 1.

Figure 1

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Dot plot of individual patient Fatigue Assessment Scale (FAS) scores at each time point after stroke. Patients with good outcome (modified Rankin Scale; mRS <2) are displayed with open circles, those with mRS are displayed with closed circles. Not every subject contributed data at each time point. The median (interquartile range) FAS for the entire cohort at each time point is displayed along the x axis.

Table 1.

Differences in clinical characteristics and genetics in those who were not tired, those who were tired, and those who were extremely tired (based on the highest observed FAS score)

FAS, 10-21, N = 17 FAS, 22-34, N = 14 FAS, 35-50, N = 8 P value
Clinical characteristics at presentation
    Age (years) 47 (40-61) 52 (48-68) 61 (50-70) .08
    Female gender, n (%) 6 (35) 8 (57) 2 (25) >.20
    Initial NIHSS score 3 (2-8) 5 (4-10) 7 (3-10) .10
    Infarct volume (mm3) 3.8 (.5-15.0) 4.4 (1.0-32.2) .8 (.2-8.6) >.20
    Endorsed sadness,* n (%) 3 (18) 7 (50) 5 (62) <.05
    Hypertension, n (%) 8 (47) 8 (57) 4 (50) >.20
    Hyperlipidemia, n (%) 13 (76) 9 (64) 7 (88) >.20
    Coronary heart disease, n (%) 4 (24) 1 (7) 1 (12) >.20
    Atrial fibrillation, n (%) 3 (18) 4 (29) 1 (12) >.20
    Diabetes mellitus, n (%) 3 (18) 3 (21) 2 (25) >.20
    Smoker, n (%) 8 (47) 4 (29) 2 (25) >.20
    Prior stroke on imaging, n (%) 5 (29) 3 (21) 1 (12) >.20
Genetic polymorphisms, n (%)
    IL1RN rs4251961 (C/T or C/C genotype) 7 (41) 8 (57) 7 (88) .03
    TLR4 299Gly/399Ile variant allele carriers 4 (24) 0 0 .04
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Abbreviations: FAS, Fatigue Assessment Scale; IL1RN, interleukin-1 receptor antagonist gene; NIHSS, National Institutes of Health Stroke Scale; TLR4, toll-like receptor-4 gene.

*

Endorsed sadness at any time point during the study likelihood ratio.

Of our 39 patients, 14 (36%) had one copy and 8 (20%) had 2 copies of the C allele of the rs4251961 SNP in IL1RN. Four (10%) of subjects carried 1 variant allele at both of the TLR4 coding SNPs. IL1RN rs4251961 C allele carriers were more prone to fatigue, whereas carriers of TLR4 variant SNPs appeared to be protected against PSF (Table 1). Figure 2 shows median FAS scores over time by IL1RN C allele carrier status and TLR4 coding variant carrier status. At both 30 and 90 days, patients with at least one rs4251961 C allele had significantly higher scores on the FAS.

Figure 2.

Figure 2

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Fatigue Assessment Scale scores at each time point based on genotype: (A) compares the scores of those with an IL1RN rs4251961 C allele to those without and (B) compares the scores of those with a functional polymorphism in TLR4 to those without. Not every subject contributed data at each time point. Statistics are by Kruskal–Wallis H test. *denotes P < .05. Box plots depict the median and interquartile range.

Discussion

Fatigue is a common and disabling symptom that affects over half of stroke survivors. Few studies have addressed the biological basis of this symptom, and to our knowledge, this study is the firstto evaluate a genetic contribution to PSF. Although this exploratory study is small, it indicates a potentially substantial role for genetic factors in PSF. Large studies found that individuals with one or more IL1RN rs4251961 C alleles (those with more fatigue in our study) had lower circulating concentrations of IL-1ra and higher concentrations of inflammatory biomarkers like IL-1β and C reactive protein.6,7 In contrast, individuals with TLR4 coding variants, who appeared to be protected from PSF in our study, had decreased responsiveness to TLR4 ligands in previous studies.8,9 Our data thus support the hypothesis that PSF might result from a proinflammatory state.

This study adds to data suggesting that fatigue is independent of functional outcome given that FAS scores were similar in those with and without a good outcome. Among patients with mild stroke, fatigue is the most common complaint and is identified as the major issue restricting their life style.15,16 It is thus not surprising that fatigue is more predictive of a lower health-related quality of life in young adults than actual neurologic dysfunction.1 Fatigue also prevents individuals who experience an otherwise good stroke outcome from returning to work, which means that there are economic and psychologic implications to poststroke fatigue.17 An additional observation in our cohort is that the degree of fatigue, as assessed by the FAS, remains remarkably constant over the year after stroke. Identification of treatments to lessen fatigue might therefore improve the quality of life.

In summary, PSF is a common sequela of stroke. The etiology of PSF is not known, but available data suggest that systemic inflammation may be important. Polymorphisms in genes that affect the immune response, such as IL1RN and TLR4, appear to influence the occurrence of fatigue and support the idea that inflammation is important in the genesis of fatigue. These findings need to be validated in a larger cohort and could provide valuable insight into possible interventions to alleviate PSF.

Acknowledgments

This study was funded by NINDS R01NS049197.

Footnotes

None of the authors have anything to disclose.

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