Genetic analyses identify brain structures related to cognitive impairment associated with elevated blood pressure

Abstract Background and aims Observational studies have linked elevated blood pressure (BP) to impaired cognitive function. However, the functional and structural changes in the brain that mediate the relationship between BP elevation and cognitive impairment remain unknown. Using observational and genetic data from large consortia, this study aimed to identify brain structures potentially associated with BP values and cognitive function. Methods and results Data on BP were integrated with 3935 brain magnetic resonance imaging-derived phenotypes (IDPs) and cognitive function defined by fluid intelligence score. Observational analyses were performed in the UK Biobank and a prospective validation cohort. Mendelian randomisation (MR) analyses used genetic data derived from the UK Biobank, International Consortium for Blood Pressure, and COGENT consortium. Mendelian randomisation analysis identified a potentially adverse causal effect of higher systolic BP on cognitive function [−0.044 standard deviation (SD); 95% confidence interval (CI) −0.066, −0.021] with the MR estimate strengthening (−0.087 SD; 95% CI −0.132, −0.042), when further adjusted for diastolic BP. Mendelian randomisation analysis found 242, 168, and 68 IDPs showing significant (false discovery rate P < 0.05) association with systolic BP, diastolic BP, and pulse pressure, respectively. Most of these IDPs were inversely associated with cognitive function in observational analysis in the UK Biobank and showed concordant effects in the validation cohort. Mendelian randomisation analysis identified relationships between cognitive function and the nine of the systolic BP-associated IDPs, including the anterior thalamic radiation, anterior corona radiata, or external capsule. Conclusion Complementary MR and observational analyses identify brain structures associated with BP, which may be responsible for the adverse effects of hypertension on cognitive performance.

[1]  C. Anderson,et al.  Blood pressure lowering and prevention of dementia: an individual patient data meta-analysis. , 2022, European heart journal.

[2]  J. Chen,et al.  Association between blood pressure levels and cognitive impairment in older women: a prospective analysis of the Women’s Health Initiative Memory Study , 2022, The Lancet. Healthy longevity.

[3]  H. Whalley,et al.  Early life predictors of late life cerebral small vessel disease in four prospective cohort studies , 2021, Brain : a journal of neurology.

[4]  G. Davey Smith,et al.  Mendelian Randomization: Concepts and Scope. , 2021, Cold Spring Harbor perspectives in medicine.

[5]  James M. Eales,et al.  Uncovering genetic mechanisms of hypertension through multi-omic analysis of the kidney , 2021, Nature Genetics.

[6]  J. Lima,et al.  Early-but Not Late-Onset Hypertension Is Related to Midlife Cognitive Function. , 2021, Hypertension.

[7]  N. Eriksson,et al.  Cerebral small vessel disease genomics and its implications across the lifespan , 2020, Nature Communications.

[8]  K. Wartolowska,et al.  Midlife blood pressure is associated with the severity of white matter hyperintensities: analysis of the UK Biobank cohort study , 2020, European heart journal.

[9]  Stephen Burgess,et al.  MendelianRandomization v0.5.0: updates to an R package for performing Mendelian randomization analyses using summarized data , 2020, Wellcome open research.

[10]  G. Lembo,et al.  Brain Functional Magnetic Resonance Imaging Highlights Altered Connections and Functional Networks in Patients With Hypertension , 2020, Hypertension.

[11]  M. Fornage,et al.  Association of blood pressure with cognitive function at midlife: a Mendelian randomization study , 2020, BMC Medical Genomics.

[12]  Naaheed Mukadam,et al.  Dementia prevention, intervention, and care: 2020 report of the Lancet Commission , 2020, The Lancet.

[13]  J. Pell,et al.  Association of SBP and BMI with cognitive and structural brain phenotypes in UK Biobank. , 2020, Journal of hypertension.

[14]  Hongtu Zhu,et al.  Genome-wide association analysis of 19,629 individuals identifies variants influencing regional brain volumes and refines their genetic co-architecture with cognitive and mental health traits , 2019, Nature Genetics.

[15]  Ian J Deary,et al.  Reliability and validity of the UK Biobank cognitive tests , 2019, PloS one.

[16]  M. Dichgans,et al.  Small vessel disease: mechanisms and clinical implications , 2019, The Lancet Neurology.

[17]  C. Iadecola,et al.  Neurovascular and Cognitive Dysfunction in Hypertension. , 2019, Circulation research.

[18]  Thomas E. Nichols,et al.  Large-scale GWAS reveals genetic architecture of brain white matter microstructure and genetic overlap with cognitive and mental health traits (n=17,706) , 2019, Molecular Psychiatry.

[19]  Martin Dichgans,et al.  WMH and long-term outcomes in ischemic stroke , 2019, Neurology.

[20]  J. Williamson,et al.  Effect of Intensive vs Standard Blood Pressure Control on Probable Dementia: A Randomized Clinical Trial , 2019, JAMA.

[21]  Mark E Bastin,et al.  Associations between vascular risk factors and brain MRI indices in UK Biobank , 2019, bioRxiv.

[22]  S. Larsson,et al.  Clinical Significance of Magnetic Resonance Imaging Markers of Vascular Brain Injury: A Systematic Review and Meta-analysis , 2019, JAMA neurology.

[23]  Mohammad Hosein Farzaei,et al.  Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017 , 2018, Lancet.

[24]  G. Douaud,et al.  An expanded set of genome-wide association studies of brain imaging phenotypes in UK Biobank , 2021, Nature neuroscience.

[25]  C. Iadecola,et al.  Hypertension, dietary salt and cognitive impairment , 2018, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[26]  J. Marchini,et al.  Genome-wide association studies of brain imaging phenotypes in UK Biobank , 2018, Nature.

[27]  Christian Gieger,et al.  Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits , 2018, Nature Genetics.

[28]  Jonathan P. Beauchamp,et al.  Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals , 2018, Nature Genetics.

[29]  G. Davey Smith,et al.  Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians , 2018, British Medical Journal.

[30]  G. Lembo,et al.  Brain MRI fiber-tracking reveals white matter alterations in hypertensive patients without damage at conventional neuroimaging , 2018, Cardiovascular research.

[31]  Valeriia Haberland,et al.  The MR-Base platform supports systematic causal inference across the human phenome , 2018, eLife.

[32]  G. Davey Smith,et al.  Orienting the causal relationship between imprecisely measured traits using GWAS summary data , 2017, PLoS genetics.

[33]  Ludovica Griffanti,et al.  Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank , 2017, NeuroImage.

[34]  Olena O Yavorska,et al.  MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data , 2017, International journal of epidemiology.

[35]  He Gao,et al.  Genome-wide association analysis identifies novel blood pressure loci and offers biological insights into cardiovascular risk , 2017, Nature Genetics.

[36]  Sudha Seshadri,et al.  Impact of Hypertension on Cognitive Function: A Scientific Statement From the American Heart Association , 2016, Hypertension.

[37]  J. Chirinos Large Artery Stiffness, Microvascular Function, and Cardiovascular Risk. , 2016, Circulation Cardiovascular Imaging.

[38]  J. Pell,et al.  Associations between single and multiple cardiometabolic diseases and cognitive abilities in 474 129 UK Biobank participants , 2017 .

[39]  I. Deary,et al.  Cognitive ability and physical health: a Mendelian randomization study , 2016, bioRxiv.

[40]  P. Matthews,et al.  Multimodal population brain imaging in the UK Biobank prospective epidemiological study , 2016, Nature Neuroscience.

[41]  Jaak Vilo,et al.  ClustVis: a web tool for visualizing clustering of multivariate data using Principal Component Analysis and heatmap , 2015, Nucleic Acids Res..

[42]  P. Elliott,et al.  UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age , 2015, PLoS medicine.

[43]  K. Jellinger,et al.  The overlap between vascular disease and Alzheimer’s disease - lessons from pathology , 2014, BMC Medicine.

[44]  Kosuke Imai,et al.  mediation: R Package for Causal Mediation Analysis , 2014 .

[45]  Benjamin S. Aribisala,et al.  Blood Pressure, Internal Carotid Artery Flow Parameters, and Age-Related White Matter Hyperintensities , 2014, Hypertension.

[46]  Evan Fletcher,et al.  Effects of systolic blood pressure on white-matter integrity in young adults in the Framingham Heart Study: a cross-sectional study , 2012, The Lancet Neurology.

[47]  Christian Gieger,et al.  Genetic Variants in Novel Pathways Influence Blood Pressure and Cardiovascular Disease Risk , 2011, Nature.

[48]  Yun Li,et al.  METAL: fast and efficient meta-analysis of genomewide association scans , 2010, Bioinform..

[49]  Jessica A. Grahn,et al.  The cognitive functions of the caudate nucleus , 2008, Progress in Neurobiology.

[50]  R. Mayeux,et al.  Hypertension and the risk of mild cognitive impairment. , 2007, Archives of neurology.

[51]  B. Winblad,et al.  The age-dependent relation of blood pressure to cognitive function and dementia , 2005, The Lancet Neurology.

[52]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[53]  M. Caulfield,et al.  White Blood Cells and Blood Pressure A Mendelian Randomization Study , 2020 .