Introduction:
Stroke is a common cause of physical disability. Women generally suffer from more severe strokes, have poorer stroke outcomes, and higher mortality than that of men. Cytokines play an important role in post-stroke inflammation. Prior studies have examined differences in individual cytokine levels in patients with acute ischemic stroke (AIS), but comprehensive cytokine expression profiling across different sex and clinical characteristics are lacking.
Hypothesis:
Stroke is a sexually dimorphic disease with well-known sex differences in immune cell prevalence, cytokine expression, and outcome. A comprehensive cytokine and immune cell network may help identify sex-specific immune response and further provide guidance for stroke research in females.
Methods:
Patients with AIS were recruited from 2011-2015 at a Comprehensive Stroke Center. Multiplex analysis (Luminex 200 IS) was used to measure serum levels of 30 common cytokines. Data were analyzed with SPSS 26.0 (IBM) and machine learning algorithms. Spearman’s correlation, Mann-Whitney U test, and two-way ANOVA analyses were used to determine the relationships among the variables. The network between cytokines and immune cell types was predicted by CIBERSORT and modified ssGSEA in R package.
Results:
We examined sex differences in serum cytokine profiles on stroke severity and immune cells profiles using 144 patients with AIS. Among 30 cytokines, IFN-A2, IFNγ, IL-1RA, IL-6, IL-8, IP-10, RANTES, TNFα, and VEGF were found to have statistically significant differences between male and female. Additionally, female survivors with higher admission NIHSS exhibited higher levels of IFN-A2, IFNγ, IL-6, and IL-8 (F=2.722, p=.011; F=2.245, p=.034; F=7.626, p<.001; F=4.599, p<.001, respectively). A cytokine-immune cell network was created using computer algorithms resulting in identification of an upregulation of Th22 in the female. Sex-specific expression of Th22 cells was then validated in human PBMC.
Conclusion:
Our study suggests sex is an important factor which determines clinical outcome. Reducing Th22 may improve stroke recovery in females. Analyzing clinical data using machine learning algorithms can identify prognostic indicators of stroke.