Sharma, Pankaj

Abstract While chronological age is one of the most influential determinants of post-stroke outcomes, little is known of the impact of neuroimaging-derived biological brain age. We here first examine whether radiomics analysis of the texture of brain T2-FLAIR MRI images can be used to predict brain age in stroke patients. We then assess the clinical determinants of accelerated brain aging and, finally, its impact on post-stroke functional outcomes. Leveraging a multisite cohort of 4,163 ischemic stroke patients, we show that older-appearing patients have more hypertension, diabetes mellitus, prior strokes, and smoking history and are more likely to develop worse post-stroke outcomes than their younger-appearing counterparts. Our results strengthen the importance of preventive medicine for maintaining brain health in stroke patients as they age and suggest a novel methodology to capture previously undescribed prognostic information available on commonly acquired MRI sequences during routine stroke care.

Background and Purpose: Family-based methods for estimating heritability cannot discriminate between shared genetic and shared environmental exposures. Recently, methods have been developed for estimating heritability in population samples using genome-wide SNPs. We used the approach developed by Visscher and colleagues to estimate the heritability of ischemic stroke in Caucasian subjects. In addition to evaluating the overall heritability of ischemic stroke, we assessed whether stroke heritability varies by age, gender, and stroke subtype. Methods: Using publicly available software (GCTA and PLINK), we estimated ischemic stroke heritability stratified by age and gender using genome-wide association (GWA) data from three Caucasian ischemic stroke studies: Ischemic Stroke Genetics Study (ISGS), Bio-Repository of DNA in Stroke (BRAINS), and Genetics of Early-Onset Stroke (GEOS). Weighted means of site-specific heritability point estimates were combined according to a standard fixed effects model. Results: Conclusions: A SNP-based approach may be useful in discerning differences in ischemic stroke heritability between different cohorts and subtypes. Overall, our analysis estimated ischemic stroke heritability to be 31% (SE = 7%), with a suggestion of higher heritability for younger cases. Small vessel stroke showed the highest heritability (58 ± 19%), with cardioembolic showing the lowest heritability (16 ± 14%). It should be emphasized that heritability estimates are population-specific and that the method used only reflects the heritability captured by common SNP variants measured in GWA studies, and not phenotypic variability explained by rare variants.