Format:
Online-Ressource
Content:
Objective To explore genetic and lifestyle risk factors of MRI-defined brain infarcts (BI) in large population-based cohorts. Methods We performed meta-analyses of genome-wide association studies (GWAS) and examined associations of vascular risk factors and their genetic risk scores (GRS) with MRI-defined BI and a subset of BI, namely, small subcortical BI (SSBI), in 18 population-based cohorts (n = 20,949) from 5 ethnicities (3,726 with BI, 2,021 with SSBI). Top loci were followed up in 7 population-based cohorts (n = 6,862; 1,483 with BI, 630 with SBBI), and we tested associations with related phenotypes including ischemic stroke and pathologically defined BI. Results The mean prevalence was 17.7% for BI and 10.5% for SSBI, steeply rising after age 65. Two loci showed genome-wide significant association with BI: FBN2, p = 1.77 × 10−8; and LINC00539/ZDHHC20, p = 5.82 × 10−9. Both have been associated with blood pressure (BP)–related phenotypes, but did not replicate in the smaller follow-up sample or show associations with related phenotypes. Age- and sex-adjusted associations with BI and SSBI were observed for BP traits (p value for BI, p[BI] = 9.38 × 10−25; p[SSBI] = 5.23 × 10−14 for hypertension), smoking (p[BI] = 4.4 × 10−10; p[SSBI] = 1.2 × 10−4), diabetes (p[BI] = 1.7 × 10−8; p[SSBI] = 2.8 × 10−3), previous cardiovascular disease (p[BI] = 1.0 × 10−18; p[SSBI] = 2.3 × 10−7), stroke (p[BI] = 3.9 × 10−69; p[SSBI] = 3.2 × 10−24), and MRI-defined white matter hyperintensity burden (p[BI] = 1.43 × 10−157; p[SSBI] = 3.16 × 10−106), but not with body mass index or cholesterol. GRS of BP traits were associated with BI and SSBI (p ≤ 0.0022), without indication of directional pleiotropy. Conclusion In this multiethnic GWAS meta-analysis, including over 20,000 population-based participants, we identified genetic risk loci for BI requiring validation once additional large datasets become available. High BP, including genetically determined, was the most significant modifiable, causal risk factor for BI.
In:
Datenlieferant: Open Access LMU (Ludwig-Maximilians-University Munich)
Language:
English
DOI:
10.1212/WNL.0000000000006851
URN:
urn:nbn:de:bvb:19-epub-72634-6
URL:
https://doi.org/10.1212/WNL.0000000000006851
URL:
https://nbn-resolving.org/urn:nbn:de:bvb:19-epub-72634-6
