Metabolomics of ICH recovery (METABOL-ICH) - Project Summary: Stroke is the third leading cause of death and the leading cause of disability in the United States. Intracerebral hemorrhage (ICH) is the subtype of stroke with the highest morbidity and mortality and demonstrates marked race/ethnic differences. Specifically, Black Americans and Hispanic Americans demonstrate approximately double the incidence rate of ICH compared to people of European descent and have their hemorrhage an average 10 years earlier in life. ICH activates several metabolic pathways involving glycogen, sphingolipids, tryptophan/kynurenine, and others. Identifying the specific metabolites that are associated with poor outcomes would provide specific targets for novel therapeutic interventions. In the current proposal, we leverage existing samples from two large genetic epidemiology studies of ICH with over-representation of disproportionately affected Black and Hispanic populations: the Ethnic/Racial Variations of Intracerebral Hemorrhage (ERICH-U01NS069763) and the Recovery and Outcomes after Stroke (ROSE-U01NS100417) studies. We will leverage 1273 non-Hispanic Black, non-Hispanic White and Hispanic ICH participants with 90-day outcomes, and demographically and geographically matched controls with plasma samples from 41 sites across the United States. Participants were prospectively recruited, underwent detailed interview, chart abstraction and neuroimaging review. We propose to utilize a comprehensive and validated metabolomic approach to assess 5400 metabolites. To these findings, we are able to bring a wealth of already phenotyped covariates including neuroimaging variables such as white matter hyperintensity and atrophy, treatments including reversal of anticoagulants and blood pressure treatment as well as subsequent events such as re-hemorrhage, infections, and cardiac events. The proposal represents the largest comprehensive assessment of metabolomics after ICH to date. A key component of our research is that features such as race, sex and volume of ICH are unalterable traits, but may have markedly different metabolomic responses which can be addressed. Understanding these differences is thus key to prioritizing targets with greatest potential impact as treatments. Finally, we will seek to identify the independent, treatable targets for future interventions by exploring the relative contributions of clinical, demographic, neuroimaging, genetic and metabolomic risk factors for outcomes to generate and test machine learning predictive models for 3-month outcomes after ICH.
