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. 2019 Jan 28;92(5):e486-e503.
doi: 10.1212/WNL.0000000000006851.

Genetic and lifestyle risk factors for MRI-defined brain infarcts in a population-based setting

Ganesh Chauhan, Hieab H H Adams, Claudia L Satizabal, Joshua C Bis, Alexander Teumer, Muralidharan Sargurupremraj, Edith Hofer, Stella Trompet, Saima Hilal, Albert Vernon Smith, Xueqiu Jian, Rainer Malik, Matthew Traylor, Sara L Pulit, Philippe Amouyel, Bernard Mazoyer, Yi-Cheng Zhu, Sara Kaffashian, Sabrina Schilling, Gary W Beecham, Thomas J Montine, Gerard D Schellenberg, Olafur Kjartansson, Vilmundur Guðnason, David S Knopman, Michael E Griswold, B Gwen Windham, Rebecca F Gottesman, Thomas H Mosley, Reinhold Schmidt, Yasaman Saba, Helena Schmidt, Fumihiko Takeuchi, Shuhei Yamaguchi, Toru Nabika, Norihiro Kato, Kumar B Rajan, Neelum T Aggarwal, Philip L De Jager, Denis A Evans, Bruce M Psaty, Jerome I Rotter, Kenneth Rice, Oscar L Lopez, Jiemin Liao, Christopher Chen, Ching-Yu Cheng, Tien Y Wong, Mohammad K Ikram, Sven J van der Lee, Najaf Amin, Vincent Chouraki, Anita L DeStefano, Hugo J Aparicio, Jose R Romero, Pauline Maillard, Charles DeCarli, Joanna M Wardlaw, Maria Del C Valdés Hernández, Michelle Luciano, David Liewald, Ian J Deary, John M Starr, Mark E Bastin, Susana Muñoz Maniega, P Eline Slagboom, Marian Beekman, Joris Deelen, Hae-Won Uh, Robin Lemmens, Henry Brodaty, Margaret J Wright, David Ames, Giorgio B Boncoraglio, Jemma C Hopewell, Ashley H Beecham, Susan H Blanton, Clinton B Wright, Ralph L Sacco, Wei Wen, Anbupalam Thalamuthu, Nicola J Armstrong, Elizabeth Chong, Peter R Schofield, John B Kwok, Jeroen van der Grond, David J Stott, Ian Ford, J Wouter Jukema, Meike W Vernooij, Albert Hofman, André G Uitterlinden, Aad van der Lugt, Katharina Wittfeld, Hans J Grabe, Norbert Hosten, Bettina von Sarnowski, Uwe Völker, Christopher Levi, Jordi Jimenez-Conde, Pankaj Sharma, Cathie L M Sudlow, Jonathan Rosand, Daniel Woo, John W Cole, James F Meschia, Agnieszka Slowik, Vincent Thijs, Arne Lindgren, Olle Melander, Raji P Grewal, Tatjana Rundek, Kathy Rexrode, Peter M Rothwell, Donna K Arnett, Christina Jern, Julie A Johnson, Oscar R Benavente, Sylvia Wasssertheil-Smoller, Jin-Moo Lee, Quenna Wong, Braxton D Mitchell, Stephen S Rich, Patrick F McArdle, Mirjam I Geerlings, Yolanda van der Graaf, Paul I W de Bakker, Folkert W Asselbergs, Velandai Srikanth, Russell Thomson, Rebekah McWhirter, Chris Moran, Michele Callisaya, Thanh Phan, Loes C A Rutten-Jacobs, Steve Bevan, Christophe Tzourio, Karen A Mather, Perminder S Sachdev, Cornelia M van Duijn, Bradford B Worrall, Martin Dichgans, Steven J Kittner, Hugh S Markus, Mohammad A Ikram, Myriam Fornage, Lenore J Launer, Sudha Seshadri, W T Longstreth Jr, Stéphanie Debette; 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
Collaborators

Genetic and lifestyle risk factors for MRI-defined brain infarcts in a population-based setting

Ganesh Chauhan et al. Neurology. .

Abstract

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.

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Figures

Figure 1
Figure 1. Prevalence of MRI-defined brain infarcts (BI) and small subcortical brain infarcts (SSBI) by different age groups
CI = confidence interval.
See this image and copyright information in PMC

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