Apr 8, 2020
Jane Ferguson: Hi there. Welcome to Getting Personal: Omics of the Heart, the podcast from Circulation: Genomic and Precision Medicine. I'm Jane Ferguson, and this is Episode 36 from February 2020.
First up, we have “Identification of Circulating Proteins Associated with Blood Pressure Using Mendelian Randomization” from Sébastien Thériault, Guillaume Paré, and colleagues from McMaster University in Ontario. They set out to assess whether they could identify protein biomarkers of hypertension using a Mendelian randomization approach. They analyzed data from a genome-wide association study of 227 biomarkers which were profiled on a custom Luminex-based platform in over 4,000 diabetic or prediabetic participants of the origin trial.
They constructed genetic predictors of each protein and then used these as instruments for Mendelian randomization. They obtained systolic and diastolic blood pressure measurements in almost 70,000 individuals, in addition to mean arterial pressure and pulse pressure in over 74,000 individuals, all European ancestry with GWAS data, as part of the International Consortium for Blood Pressure.
Out of the 227 biomarkers tested, six of them were significantly associated with blood pressure traits by Mendelian randomization after correction for multiple testing. These included known biomarkers such as NT-proBNP, but also novel associations including urokinase-type plasminogen activator, adrenomedullin, interleukin-16, cellular fibronectin and insulin-like growth factor binding protein-3. They validated all of the associations apart from IL-16 in over 300,000 participants in UK Biobank. They probed associations with other cardiovascular risk markers and found that NT-proBNP associated with large artery atherosclerotic stroke, IGFBP3 associated with diabetes, and CFN associated with body mass index.
This study identified novel biomarkers of blood pressure, which may be causal in hypertension. Further study of the underlying mechanisms is required to understand whether these could be useful therapeutic targets in hypertensive disease.
The next paper comes from Sony Tuteja, Dan Rader, Jay Giri and colleagues from the University of Pennsylvania and it's entitled, “Prospective CYP2C19 Genotyping to Guide Antiplatelet Therapy Following Percutaneous Coronary Intervention: A Pragmatic Randomized Clinical Trial”.
They designed a pharmacode genomic trial to assess effects of CYP2C19 genotyping on antiplatelet therapy following PCI. Because loss of function alleles in CYP2C19 impair the effectiveness of clopidogrel, the team were interested in understanding whether knowledge of genotype status would affect prescribing in a clinical setting. They randomized 504 participants to genotype guided or usual care groups and assessed the rate of prasugrel or ticagrelor prescribing in place of clopidogrel within each arm. As a secondary outcome, they assessed whether prescribers adhere to genotype guided recommendations. Of genotyped individuals, 28% carried loss of function alleles. Within the genotype guided group overall, there was higher use of prasugrel or ticagrelor with these being prescribed to 30% of patients compared with only 21% in the usual care group. Within genotype individuals carrying loss of function alleles, 53% were started on prasugrel or ticagrelor, demonstrating some adherence to genotype guided recommendations.
However, this also meant that 47% of people whose genotype suggested reduced effectiveness were nevertheless prescribed clopidogrel. This study highlights that even when genotype information is available, interventional cardiologists consider clinical factors such as disease presentation and may weight these more highly than genotype information when selecting antiplatelet therapy following PCI.
The next paper is about “Deep Mutational Scan of an SCN5A Voltage Sensor and comes to us from Andrew Glazer, Dan Roden and colleagues from Vanderbilt University Medical Center. In this paper, the team aim to characterize the functional consequences of variants and the S4 voltage sensor of domain IV and the SCN5A gene using a high throughput method that they developed. SCN5A encodes the major voltage gated sodium channel in the heart and variants in SCN5A can cause multiple distinct genetic arrhythmia syndromes, including Brugada syndrome, long QT syndrome, atrial fibrillation, and dilated cardiomyopathy, and have been linked to sudden cardiac death.
Because of this, there's considerable interest in understanding the functional and clinical consequences of different variants, but previous approaches were time consuming and results were often inconclusive with many variants being classified as uncertain significance. This newly developed deep mutational scanning approach allows for simultaneous assessment of the function of thousands of variants, making it much more efficient than low throughput patch clamping. The team assessed the function of 248 variants using a triple drug assay in HEK293T cells expressing each variant and they identified 40 putative gain of function and 33 putative loss of function variants. They successfully validated eight of nine of these by patch clamping data. Their study highlights the effectiveness of this deep mutational scanning approach for investigating variants in the cardiac sodium channel SCN5A gene and suggests that this may also be an effective approach for investigating putative disease variants and other ion channels.
The next article is a research letter from Connor Emdin, Amit Khera, and colleagues from Mass General Hospital in the Broad Institute entitled, “Genome-Wide Polygenic Score and Cardiovascular Outcomes with Evacetrapib in Patients with High-Risk Vascular Disease: A Nested Case-Control Study”. In this study, the team set out to probe the utility of using polygenic risk scores to predict the risk of major adverse cardiovascular events within individuals already known to be at high cardiovascular risk and to assess whether genetic scores can identify individuals who would benefit from the use of a CETP inhibitor such as Evacetrapib. They analyze data from the ACCELERATE trial which had tested Evacetrapib in a high risk population, and they found no effect on the incidents of major adverse cardiovascular events overall. Within a nested case-control sample of individuals experiencing major CVD events versus no events, they applied a polygenic risk score and found that the score predicted major cardiovascular events.
Patients in the highest quintile of the risk score were at 60% higher risk of a major cardiovascular event than patients in the lowest quintile. There was no evidence of any interaction between the genetic risk score and Evacetrapib. These data suggest that genetic risk scores may have utility in identifying individuals at high risk events but may not have utility in identifying individuals who may derive more benefit from CETP inhibition.
The next letter concerns “Epigenome-Wide Association Study Identifies a Novel DNA Methylation in Patients with Severe Aortic Valve Stenosis” and comes from Takahito Nasu, Mamoru Satoh, Makoto Sasaki and colleagues from Iwate Medical University in Japan. They were interested in understanding whether differences in DNA methylation could underlie the risk of aortic valve stenosis. They conducted an EWAS or epigenome-wide association study of peripheral blood mononuclear cells or PBMCs from 44 individuals with aortic stenosis and 44 disease free controls.
They collected samples at baseline before a surgical intervention in the individuals with aortic stenosis and collected a follow-up sample one year later. They found that DNA methylation at a site on chromosome eight mapping to the TRIB1, or tribbles homolog one gene, was lower in the aortic stenosis group than in the controls at baseline. They replicated the association in an independent sample of 50 cases and 50 controls. TRIB1 MRNA levels were higher in the aortic stenosis group than the controls. When they looked at methylation status one year after aortic valve replacement or a transcatheter aortic valve implantation in patients with stenosis, they found that DNA methylation had increased in the cases while TRIB1 MRNA decreased. These data suggests that methylation status of TRIB1 and expression of TRIB1 may relate to the disease processes in aortic stenosis such as hemodynamic dysregulation and they can be reversed through surgical intervention. Changes in the methylation status of TRIB1 could be a novel biomarker of response to aortic valve replacement.
The next letter comes from Niels Grote Beverborg, Pim van der Harst, and colleagues from University Medical Center Groningen and is entitled, “Genetically Determined High Levels of Iron Parameters Are Protective for Coronary Artery Disease”. Their study addresses the conflicting hypotheses that high iron status is either deleterious or protective against cardiovascular disease. The team constructed genetic predictors of serum iron status using 11 previously identified snips and tested the genetic association with CAD in UK Biobank data from over 408,000 white participants. Overall, the genetic score for higher iron status was associated with protection against CAD. Ten of the snips suggested individual neutral or protective effects of higher iron status on CAD, while one iron increasing snip was associated with increased risk of disease but this was thought to be likely through an iron independent mechanism. Overall, these data suggest that a genetic predisposition to higher iron status does not increase risk of CAD and is actually protective against disease.
The final letter is entitled, “Confidence Weighting for Robust Automated Measurements of Popliteal Vessel Wall MRI” and comes from Daniel Hippe, Jenq-Neng Hwang, and colleagues from the University of Washington. They were interested in assessing whether images of popliteal artery wall incidentally obtained during knee MRI as part of an osteoarthritis study could be used to study the development and progression of atherosclerosis. They developed an automated deep learning based algorithm to segment and quantify the popliteal artery wall in images obtained over 10 years in over 4,700 individuals. Their approach, which they named FRAPPE, or fully automated and robust analysis technique for popliteal artery evaluation, was able to reduce the average time required for segmentation analysis from four hours to eight minutes per image. They applied weights based on confidence for each segment to automatically improve the accuracy of aggregate measurements such as mean wall thickness or mean lumen area. Their data suggest that this automated method can rapidly generate useful information on atherosclerosis from MRI images obtained as part of other studies. When combined with other data. This approach may facilitate novel discovery in secondary analyses of existing studies in an efficient and cost effective way.
And that's all for issue one of 2020. Come back next time for more of the latest papers from Circulation: Genomic and Precision Medicine.
Speaker 2: This podcast is copyright American Heart Association 2020.