Abstract MPWE44: A multi-ancestry blood metabolites atlas of incident stroke in ~39,000 adults

Abstract

Background: A comprehensive blood metabolome fingerprint of incident stroke and the temporal variation of metabolite levels across pre-diagnostic trajectories remain poorly defined. Methods: We included 38,594 stroke-free adults from seven multi-ethnic TOPMed cohorts with 1,245 named circulating metabolites ( Fig1.a ). Incident stroke (n = 1,628) was ascertained over 7.5–19.3 years. Results from cohort-specific Cox models [adjusted for sociodemographic characteristics, behavioral factors and medications use] were pooled by random-effects meta-analysis. We examined race/ethnicity-specific associations, pre-diagnostic trajectories of metabolites, and risk prediction with metabolite panels. Results: We identified 141 metabolites (FDR < 0.05) associated with incident stroke by pooling results from 7 cohorts after multivariate adjustment, with 97 metabolites independent of major cardiometabolic traits (e.g., glucose, lipids, blood pressures; Fig1.b ). Over 80% of identified metabolites showed positive associations, predominantly belonging to phosphatidyl lipids, steroids, glutamate/glutamyl amino acids, aromatic amino acids, and ceramides ( Fig1.b,c ). Race/ethnicity stratified analyses revealed 21 out of 141 identified metabolites (FDR<0.05) exhibiting significant heterogeneities in associations with stroke across ancestry groups. Of note, 9 metabolites (e.g., MTA, HPLA, oxalate) showed stronger or even opposite associations in Hispanics compared to other ancestry groups ( Fig1.d ). In Study of Latinos (SOL, n=13,453), temporal analysis identified 3 clusters of metabolites exhibiting nonlinear variational patterns in levels throughout 12 years before stroke diagnosis. Furthermore, higher weighted scores for metabolites in clusters 1&2 were linked with elevated risk of stroke, whereas a reduced risk was found for cluster 3, in which metabolites maintained comparatively low levels throughout the pre-diagnostic process of stroke ( Fig.1e ). Adding metabolites to conventional risk factors significantly improved risk prediction of incident stroke (AUC improved from 0.78 to 0.84; p<0.001) ( Fig1.f ). Conclusion: Our study characterized most comprehensive metabolomic signatures of incident stroke to date and revealed potentially ancestry specific signals. Our results further characterized complex temporal dynamics of identified metabolites across pre-diagnostic process and reinforced the value of metabolites in stroke risk prediction.