3393 Biomarkers of Stroke Recovery Study. (27th March 2019)

• Record Type: Journal Article
• Authors: Edwardson, Matthew A.; Cheema, Amrita; Tan, Ming; Dromerick, Alexander
• Is Part Of: Journal of clinical and translational science. Volume 3(2019)Supplement 1
• Page Start: 35
• Page End: 36
• Publication Date: 2019-03-27
• Subjects: Clinical medicine -- Research -- Periodicals; Medicine, Experimental -- Periodicals; Human experimentation in medicine -- Periodicals; 616.027
• DOI: 10.1017/cts.2019.86
• Languages: English
• ISSNs: 2059-8661

Abstract

Abstract : OBJECTIVES/SPECIFIC AIMS: There are currently no established blood-based biomarkers of recovery and neural repair following stroke in humans. Such biomarkers would be extremely valuable for aiding in stroke prognosis, timing rehabilitation therapies, and designing drugs to augment natural repair mechanisms. Metabolites, including lipids and amino acids, are engaged in many cellular processes and cross the blood-brain barrier more easily than proteins. Recent advances in liquid chromatography / mass spectrometry (LCMS) allow researchers to obtain a biochemical fingerprint of the metabolites in various biofluids. Thus, metabolite biomarkers of neural repair after brain injury are a promising avenue for future research. Objective: Design and conduct a study to identify metabolite changes in the blood associated with good and poor motor recovery following stroke. METHODS/STUDY POPULATION: We launched the Biomarkers of Stroke Recovery (BIOREC) study, which seeks to enroll 70 participants suffering arm motor impairment following stroke and 35 matched controls. BIOREC is a longitudinal observational study. Fasting blood samples are collected at 5, 15, and 30 days post-stroke, processed, and stored in the Georgetown Lombardi biorepository. Outcome measures, including measures of motor impairment, cognition and language, are assessed at 5, 15, 30, and 90 days post-stroke. The primary outcome measure is the upper extremity Fugl-Meyer score. Control participants are matched for age +/− 1 yr, race, gender, cardiovascular comorbities, and statin use through a computer algorithm that screens the entire MedStar electronic health record (EHR). Control participants provide 2 fasting blood samples one month apart. Once all samples are collected and sent for LCMS analysis, logistic regression analysis will identify potential metabolite biomarkers by comparing participants with good recovery to those with poor recovery as well as stroke participants to controls. RESULTS/ANTICIPATED RESULTS: To date, forty stroke participants have enrolled from 4 acute care hospitals in the Washington, DC metro region and completed all study procedures. Twenty stroke participants either dropped out or were withdrawn due to other medical concerns. Stroke patients ended up at a variety of venues following their acute hospitalization including the acute rehabilitation hospital, skilled nursing facilities, and home. We learned to overcome these logistical challenges by traveling to wherever the patients were sent and notifying medical providers of their study participation. In rare cases we have paid to transport patients from skilled nursing facilities to the clinic, which has reduced dropouts. In addition to the stroke participants, we have enrolled 7 healthy control participants using the EHR screening algorithm. DISCUSSION/SIGNIFICANCE OF IMPACT: Performing a longitudinal study in the early recovery phase following stroke is logistically challenging, but feasible. Difficulty in identifying participants with isolated motor impairment requires added effort to eliminate dropouts. Screening the EHR is an effective method to identify matched controls. Future metabolomics analysis of stored blood samples holds promise to identify biomarkers of stroke recovery and neural repair.