학술논문

Genetic and lifestyle risk factors for MRI-defined brain infarcts in a population-based setting
Document Type
article
Author
Chauhan, GaneshAdams, Hieab HHSatizabal, Claudia LBis, Joshua CTeumer, AlexanderSargurupremraj, MuralidharanHofer, EdithTrompet, StellaHilal, SaimaSmith, Albert VernonJian, XueqiuMalik, RainerTraylor, MatthewPulit, Sara LAmouyel, PhilippeMazoyer, BernardZhu, Yi-ChengKaffashian, SaraSchilling, SabrinaBeecham, Gary WMontine, Thomas JSchellenberg, Gerard DKjartansson, OlafurGuðnason, VilmundurKnopman, David SGriswold, Michael EWindham, B GwenGottesman, Rebecca FMosley, Thomas HSchmidt, ReinholdSaba, YasamanSchmidt, HelenaTakeuchi, FumihikoYamaguchi, ShuheiNabika, ToruKato, NorihiroRajan, Kumar BAggarwal, Neelum TDe Jager, Philip LEvans, Denis APsaty, Bruce MRotter, Jerome IRice, KennethLopez, Oscar LLiao, JieminChen, ChristopherCheng, Ching-YuWong, Tien YIkram, Mohammad Kvan der Lee, Sven JAmin, NajafChouraki, VincentDeStefano, Anita LAparicio, Hugo JRomero, Jose RMaillard, PaulineDeCarli, CharlesWardlaw, Joanna Mdel C. Valdés Hernández, MariaLuciano, MichelleLiewald, DavidDeary, Ian JStarr, John MBastin, Mark EManiega, Susana MuñozSlagboom, P ElineBeekman, MarianDeelen, JorisUh, Hae-WonLemmens, RobinBrodaty, HenryWright, Margaret JAmes, DavidBoncoraglio, Giorgio BHopewell, Jemma CBeecham, Ashley HBlanton, Susan HWright, Clinton BSacco, Ralph LWen, WeiThalamuthu, AnbupalamArmstrong, Nicola JChong, ElizabethSchofield, Peter RKwok, John Bvan der Grond, JeroenStott, David JFord, IanJukema, J WouterVernooij, Meike WHofman, AlbertUitterlinden, André Gvan der Lugt, AadWittfeld, KatharinaGrabe, Hans JHosten, Norbertvon Sarnowski, BettinaVölker, UweLevi, ChristopherJimenez-Conde, Jordi
Source
Neurology. 92(5)
Subject
Biomedical and Clinical Sciences
Neurosciences
Clinical Sciences
Stroke
Brain Disorders
Human Genome
Cardiovascular
Aging
Prevention
Genetics
Cerebrovascular
2.1 Biological and endogenous factors
Good Health and Well Being
Stroke Genetics Network (SiGN)
the International Stroke Genetics Consortium (ISGC)
METASTROKE
Alzheimer's Disease Genetics Consortium (ADGC)
and the Neurology Working Group of the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium
Cognitive Sciences
Neurology & Neurosurgery
Clinical sciences
Language
Abstract
ObjectiveTo explore genetic and lifestyle risk factors of MRI-defined brain infarcts (BI) in large population-based cohorts.MethodsWe 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.ResultsThe 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.ConclusionIn 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.