ABSTRACT NUMBER: ESOC2026A2165 CIRCULATING METABOLITE LEVELS CORRELATE WITH ISCHEMIC STROKE SEVERITY DIFFERENTIATING LARGE ARTERY ATHEROSCLEROSIS AND CRYPTOGENIC STROKE SUBTYPES

Abstract Background and aims The aim of the study was to identify serum metabolites which show associations with stroke severity in ischemic stroke patients with large artery atherosclerosis (LAA) and cryptogenic stroke (CS). Methods Metabolic profiling of the serum of patients from the Estonian Young Stroke Registry (aged 15-54 years) gathered within 72 hours from stroke onset was performed using mass-spectrometry and the BIOCRATES MxP® Quant 500 kit. Stroke severity was classified using the NIH Stroke Scale (NIHSS). Spearman correlation with NIHSS score at stroke onset was calculated. The TOAST classification was used for etiologic subtyping. Results In total, 51 (38%) had LAA and 85 (63%) had CS; patients with LAA were older than those with CS (median 49 [IQR 47–52] vs 45 [IQR 38–50], p=0.001), while stroke severity at onset was similar between groups. LAA strokes had more metabolites with significant correlations to severity than CS (41 (7%) in LAA, 18 (3%) in CS). LAA showed more correlations with lipids, especially positive correlations with medium chain acylcarnitines such as decadienoylcarnitine (r= 0.52) and ether lipids from the diacylglycerol (e.g. DG_O_14:0 18:2, r=0.4) and phospholipid (e.g. PC-O-38:1, r=0.4) classes. Negative correlations were found with triacylglycerols and bile acids. CS mainly correlated negatively with amino acids such as proline (r=-0.47) and alanine (r=-0.34). Conclusions According to our study, there are more metabolites correlating with stroke severity in subjects with LAA than in CS with several metabolites differentiating between these two subtypes. Further investigations are warranted. Conflict of interest Liisa Kõrv: Funding from Estonian research Council Grant no PRG1915; Riina Vibo: Funding from Estonian research Council Grant no PRG1915.; Kalle Kilk: Nothing to disclose; Mihkel Zilmer: Nothing to disclose; Janika Kõrv: Funding from Estonian research Council Grant no PRG1915.