Excretion of albumin in urine, or albuminuria, is associated with the development of multiple cardiovascular and metabolic diseases. However, whether pathways leading to albuminuria are causal for cardiometabolic diseases is unclear. We addressed this question using a Mendelian randomization framework in the UK Biobank, a large population-based cohort. We first performed a genome-wide association study for albuminuria in 382,500 individuals and identified 32 new albuminuria loci. We constructed albuminuria genetic risk scores and tested for association with cardiometabolic diseases. Genetically elevated albuminuria was strongly associated with increased risk of hypertension (1.38 OR; 95% CI, 1.27-1.50 per 1 SD predicted increase in albuminuria, p = 7.01e-14). We then examined bidirectional associations of albuminuria with blood pressure which suggested that genetically elevated albuminuria led to higher blood pressure (2.16 mmHg systolic blood pressure; 95% CI, 1.51-2.82 per 1 SD predicted increase in albuminuria, p = 1.22e-10) and that genetically elevated blood pressure led to more albuminuria (0.005 SD; 95% CI 0.004-0.006 per 1 mmHg predicted increase in systolic blood pressure, p = 2.45e-13). These results support the existence of a feed-forward loop between albuminuria and blood pressure and imply that albuminuria could increase risk of cardiovascular disease through blood pressure. Moreover, they suggest therapies that target albuminuria-increasing processes could have antihypertensive effects that are amplified through inhibition of this feed-forward loop.
Exome Sequencing of All Premature Coronary Artery Disease Participants in UK Biobank
Coronary artery disease (CAD) is the leading cause of death in the UK. When CAD occurs prematurely, the role for inheritance is greater. DNA sequencing of the protein-coding portions of the human genome ('the exome') can identify genes responsible for CAD. Here, we seek to: 1) identify all individuals in the UK Biobank with premature CAD (mean=55y, women=65y); 2) identify controls free of CAD; 3) perform whole exome sequencing on cases and controls; 4) compare sequences to discover genes responsible for CAD; 5) perform a comprehensive phenotypic scan to understand the spectrum of consequences from CAD genes. A stated purpose of UK Biobank is to improve the prevention, diagnosis and treatment of a wide range of serious and life-threatening illnesses. We have secured funding to exome sequence up to 20,000 UK Biobank participants with and without CAD. Successful completion of this study should result in the identification of novel genetic causes for MI, the leading cause of death in the UK. Genomic variation discovered in the UK Biobank associated with MI may prove useful to target preventive strategies, understand the biology of MI in humans, and to identify novel molecular targets for therapy. We propose to: 1) identify all individuals in the UK Biobank with CAD at an early age (=55 years old in men and =65 years old in women); 2) identify controls free of CAD; 3) perform whole exome sequencing on all cases and controls; 4) compare sequences of cases with controls to discover genes responsible for CAD; and 5) understand the range of phenotypic effects from genes associated with CAD. We have secured funding to exome sequence up to 20,000 UK Biobank participants. Of note, we have secured funding to exome sequence up to 20,000 UK Biobank participants. We seek to identify all individuals in the UK Biobank with CAD at an early age (=55 years old in men and =65 years old in women). In the latest data release, there are 10,450 participants with any diagnosis code for ischemic heart disease. Further work will be required to confirm this diagnosis and restrict to CAD onset at an early age.
|Lead investigator:||Dr Amit Khera|
|Lead institution:||Broad Institute|
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|3725||7089||Deep Learning to Predict Cardiac Magnetic Resonance-Derived Left Ventricular Mass and Hypertrophy From 12-Lead ECGs||2 Aug 2021|
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|3381||Genetic Association of Albuminuria with Cardiometabolic Disease and Blood Pressure||Haas, M.E.||2018||American Journal of Human Genetics. 2018|