Abstract
Objective: Leveraging large-scale genetic data, we aimed to identify shared pathogenic mechanisms and causal relationships between impaired kidney function and cerebrovascular disease phenotypes. Methods We used summary statistics from genome-wide association studies (GWAS) of kidney function traits (chronic kidney disease diagnosis, estimated glomerular filtration rate [eGFR], and urinary albumin-to-creatinine ratio [UACR]) and cerebrovascular disease phenotypes (ischemic stroke and its subtypes, intracerebral hemorrhage [ICH], and white matter hyperintensities [WMH] on brain MRI). We (1) tested the genetic overlap between them with polygenic risk scores (PRS), (2) searched for common pleiotropic loci with pairwise GWAS analyses, and (3) explored causal associations by employing 2-sample Mendelian randomization. Results A PRS for lower eGFR was associated with higher large artery stroke (LAS) risk (p= 1 x 10(-4)). Multiple pleiotropic loci were identified between kidney function traits and cerebrovascular disease phenotypes, with 12q24 associated with eGFR and both LAS and small vessel stroke (SVS), and 2q33 associated with UACR and both SVS and WMH. Mendelian randomization revealed associations of both lower eGFR (odds ratio [OR] per 1-log decrement, 2.10;95% confidence interval [CI], 1.38-3.21) and higher UACR (OR per 1-log increment, 2.35;95% CI, 1.12-4.94) with a higher risk of LAS, as well as between higher UACR and higher risk of ICH. Conclusions: Impaired kidney function, as assessed by decreased eGFR and increased UACR, may be causally involved in the pathogenesis of LAS. Increased UACR, previously proposed as a marker of systemic small vessel disease, is involved in ICH risk and shares a genetic risk factor at 2q33 with manifestations of cerebral small vessel disease.
Dokumententyp: | Zeitschriftenartikel |
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Fakultät: | Medizin
Medizin > Munich Cluster for Systems Neurology (SyNergy) |
Themengebiete: | 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit |
ISSN: | 0028-3878 |
Sprache: | Englisch |
Dokumenten ID: | 87168 |
Datum der Veröffentlichung auf Open Access LMU: | 25. Jan. 2022, 09:23 |
Letzte Änderungen: | 14. Jun. 2024, 06:43 |
DFG: | Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 390857198 |