Abstract TP232: A Biomarker Algorithm that Represents Time from Stroke Symptom Onset

Abstract

Background and Purpose: Time of onset is critical when treating ischemic stroke (IS). The purpose of this project was to investigate the use of our 9 gene profile to develop a biomarker algorithm that represents time from stroke onset for use in the clinical setting to improve utilization of tissue plasminogen activator (tPA) and streamline appropriate secondary prevention. Methods: Peripheral blood samples were collected from n=34 IS patients’ ≥18 years of age within 24 hours from symptom onset and 24-48 hours later. Total RNA was extracted from whole blood in Paxgene RNA tubes, amplified, and hybridized to Illumina HumanRef-8v2 bead chips. Gene expression was compared in a univariate manner between patients at both time points using t -test in GeneSpring. Inflation of type one error was corrected by Bonferroni. A linear regression was used to model the change in gene expression as a linear function of time when controlling for age. Results: The mean age of the sample was 71.9± (14.6 sd ) years. Mean time from symptom onset to acute blood draw was 9:29± (6:2 sd ) hours (range 2:35-23:02); to follow up blood draw was 29:24± (7.1 sd ) hours (range 18:45-43:30); and time between acute and follow up blood draw was 19:55± (3.3 sd ) hours (range 13:30-27:32). CA4 and ARG1 expression significantly decreased >1.5 fold, and LY96 expression by >2-fold between baseline and follow up. This decrease in expression was associated with an increase from time of stroke onset and remained significant for only LY96 expression when controlling for age. ARG1 and CA4 expression were significantly lower in older patients. Conclusions: Our profile provides evidence that the expression of LY96 , CA4 , and ARG1 in the peripheral blood may serve as a surrogate for determining the time of stroke onset. In clinical practice, an algorithm based on this biomarker profile and other clinical covariates could be used when time of onset is unknown. To increase the accuracy of our biomarker algorithm, it will be important to determine the effects of age, stroke severity, and other clinical covariates on the expression of these genes over time.