Abstract Body: Introduction: Blood-based biomarker investigations for neurological diseases such as ischemic stroke are commonly used in clinical practice and traditionally have focused on measuring alterations in cytokine quantity and composition. An emerging area has been the use of microRNA studying the effects of gene expression after ischemic stroke. There has been extensive evidence that gene expression changes after ischemic injury, especially in gene pathways related to inflammatory response, vascular injury, and cellular degeneration. Establishing a reliable blood-based biomarker used to quantify clinical indications is necessary for emergent treatment. With a blood-based assessment that utilizes transcriptomics, healthcare providers can better understand post-stroke molecular changes more rapidly, which is crucial during this acute phase.
Methods: Peripheral blood samples were collected from ischemic stroke patients (N=100) during the acute onset of disease, 24 hours after the last known well. In this study, we investigated possible differences among the stroke population based on risk factors including age, sex, race, social isolation status, and presence of cognitive impairment. For this analysis, overall social isolation status was defined by a mixed model matrix comprised of Lubben Social Network score and UCLA Loneliness score. We used whole transcriptomic sequencing to assess these variables.
Results: Overall, there were 8 miRNA sequences significantly upregulated in socially isolated patients. There were no significant differences found among patients based on sex or age variability. Interestingly, we found that circulatory miR-24-3p (FDR adjusted p-value <0.05) was significantly upregulated in post-stroke socially isolated patients and post-stroke socially isolated mice. Other candidates such as miR-143-3p, miR-629-5p, miR-223-5p, and miR-331-3p showed significantly different expressions among this cohort.
Conclusion: Our results show there is a significant difference in circulatory miRNA signature among SI and non-SI patients. The clinical translation of this identified upregulation among this vulnerable population must be explored further to characterize the nature of these changes in gene expression. These results can be utilized in the future as a reliable biomarker of SI status. Future studies using these results should be translated into mice models to understand the biomolecular mechanisms behind SI-induced changes within microRNA signatures.