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Background: Genetic studies may help identify causal pathways; therefore, we sought to identify genetic determinants of ideal CVH and their association with CVD outcomes in the multi-population Veteran Administration Million Veteran Program. Methods: An ideal health score (IHS) was calculated from 3 clinical factors (blood pressure, total cholesterol, and blood glucose levels) and 3 behavioral factors (smoking status, physical activity, and BMI), ascertained at baseline. Multi-population genome-wide association study (GWAS) was performed on IHS and binary ideal health using linear and logistic regression, respectively. Using the genome-wide significant SNPs from the IHS GWAS, we created a weighted IHS polygenic risk score (PRSIHS) which was used (i) to conduct a phenome-wide association study (PheWAS) of associations between PRSIHS and ICD-9 phenotypes and (ii) to further test for associations with mortality and selected CVD outcomes using logistic and Cox regression and, as an instrumental variable, in Mendelian Randomization. Results: The discovery and replication cohorts consisted of 142,404 (119,129 European American (EUR); 16,495 African American (AFR)), and 45,766 (37,646 EUR; 5,366 AFR) participants, respectively. The mean age was 65.8 years (SD = 11.2) and 92.7% were male. Overall, 4.2% exhibited ideal CVH based on the clinical and behavioral factors. In the multi-population meta-analysis, variants at 17 loci were associated with IHS and each had known GWAS associations with multiple components of the IHS. PheWAS analysis in 456,026 participants showed that increased PRSIHS was associated with a lower odds ratio for many CVD outcomes and risk factors. Both IHS and PRSIHS measures of ideal CVH were associated with significantly less CVD outcomes and CVD mortality. Conclusion: A set of high interest genetic variants contribute to the presence of ideal CVH in a multi-ethnic cohort of US Veterans. Genetically influenced ideal CVH is associated with lower odds of CVD outcomes and mortality. [ABSTRACT FROM AUTHOR]

Background: In response to the novel Coronavirus Disease 2019 (COVID-19) pandemic, the Department of Veterans Affairs (VA) Million Veteran Program (MVP) organized efforts to better understand the impact of COVID-19 on Veterans by developing and deploying a self-reported survey. Methods: The MVP COVID-19 Survey was developed to collect COVID-19 specific elements including symptoms, diagnosis, hospitalization, behavioral and psychosocial factors and to augment existing MVP data with longitudinal collection of key domains in physical and mental health. Due to the rapidly evolving nature of the pandemic, a multipronged strategy was implemented to widely disseminate the COVID-19 Survey and capture data using both the online platform and mailings. Results: We limited the findings of this paper to the initial phase of survey dissemination which began in May 2020. A total of 729,625 eligible MVP Veterans were invited to complete version 1 of the COVID-19 Survey. As of October 31, 2020, 58,159 surveys have been returned. The mean and standard deviation (SD) age of responders was 71 (11) years, 8.6% were female, 8.2% were Black, 5.6% were Hispanic, and 446 (0.8%) self-reported a COVID-19 diagnosis. Over 90% of responders reported wearing masks, practicing social distancing, and frequent hand washing. Conclusion: The MVP COVID-19 Survey provides a systematic collection of data regarding COVID-19 behaviors among Veterans and represents one of the first large-scale, national surveillance efforts of COVID-19 in the Veteran population. Continued work will examine the overall response to the survey with comparison to available VA health record data. [ABSTRACT FROM AUTHOR]

People with insulin resistance also appear to have greater than normal amounts of a natural substance in the body called asymmetric dimethylarginine. This substance prevents the formation of nitric oxide, which is a natural substance that relaxes blood vessels. Insulin resistance is a precursor of diabetes., Context Increased levels of asymmetric dimethylarginine (ADMA) are associated with endothelial dysfunction and increased risk of cardiovascular disease. Several cardiovascular risk factors are associated with reduced sensitivity to insulin, but elevated ADMA concentrations have not been fully linked to the metabolic syndrome. Objective To evaluate the relationship between insulin sensitivity and plasma ADMA concentrations, and to determine whether a pharmacological treatment that increases insulin sensitivity would also modulate ADMA concentrations. Design, Setting, and Subjects Cross-sectional study, containing a nonrandomized controlled trial component, of 64 healthy volunteers without diabetes (42 women, 22 men; 48 with normal blood pressure and 16 with hypertension), which was conducted at a university medical center between October 2000 and July 2001. Intervention Rosiglitazone (4 mg/d for 4 weeks and then 4 mg twice daily for 8 weeks), an insulin-sensitizing agent, was given to 7 insulin-resistant subjects with hypertension. These subjects were studied before and after 12-week treatment. Main Outcome Measures Insulin sensitivity measured by the insulin suppression test, and fasting plasma levels of low-density lipoprotein cholesterol, triglycerides, high-density lipoprotein cholesterol, glucose, insulin, and ADMA concentrations. Results Plasma ADMA concentrations were positively correlated with impairment of insulin-mediated glucose disposal in nondiabetic, normotensive subjects (r = 0.73; P Conclusion A significant relationship exists between insulin resistance and plasma concentrations of ADMA. Pharmacological intervention with rosiglitazone enhanced insulin sensitivity and reduced ADMA levels. Increases in plasma ADMA concentrations may contribute to the endothelial dysfunction observed in insulin-resistant patients.

We provide anatomic and functional evidence that nicotine induces angiogenesis. We also show that nicotine accelerates the growth of tumor and atheroma in association with increased neovascularization. Nicotine increased endothelial-cell growth and tube formation in vitro, and accelerated fibrovascular growth in vivo. In a mouse model of hind-limb ischemia, nicotine increased capillary and collateral growth, and enhanced tissue perfusion. In mouse models of lung cancer and atherosclerosis, we found that nicotine enhanced lesion growth in association with an increase in lesion vascularity. These effects of nicotine were mediated through nicotinic acetylcholine receptors at nicotine concentrations that are pathophysiologically relevant. The endothelial production of nitric oxide, prostacyclin and vascular endothelial growth factor might have a role in these effects., Author(s): Christopher Heeschen [1]; James J. Jang [1]; Michael Weis [1]; Anjali Pathak [1]; Shuichiro Kaji [1]; Robert S. Hu [1]; Philip S. Tsao [1]; Frances L. Johnson [1]; John [...]

Genetically induced hypercholesterolemia attenuates aerobic capacity in mice, but the latter is improved by exercise training. However, while exercise training improves aerobic capacity to the same degree in both normal and genetically hypercholesterolemic mice, there remains a difference between these two groups after training. Hypercholesterolemia's impairment of vascular nitric oxide synthesis and/or vascular smooth muscle sensitivity to nitrovasodilators may be related to its adverse impact on aerobic capacity.

Although affecting different arterial territories, the related atherosclerotic vascular diseases coronary artery disease (CAD) and peripheral artery disease (PAD) share similar risk factors and have shared pathobiology. To identify novel pleiotropic loci associated with atherosclerosis, we performed a joint analysis of their shared genetic architecture, along with that of common risk factors. Using summary statistics from genome-wide association studies of nine known atherosclerotic (CAD, PAD) and atherosclerosis risk factors (body mass index, smoking initiation, type 2 diabetes, low density lipoprotein, high density lipoprotein, total cholesterol, and triglycerides), we perform 15 separate multi-trait genetic association scans which resulted in 25 novel pleiotropic loci not yet reported as genome-wide significant for their respective traits. Colocalization with single-tissue eQTLs identified candidate causal genes at 14 of the detected signals. Notably, the signal between PAD and LDL-C at the PCSK6 locus affects PCSK6 splicing in human liver tissue and induced pluripotent derived hepatocyte-like cells. These results show that joint analysis of related atherosclerotic disease traits and their risk factors allowed identification of unified biology that may offer the opportunity for therapeutic manipulation. The signal at PCSK6 represent possible shared causal biology where existing inhibitors may be able to be leveraged for novel therapies. [ABSTRACT FROM AUTHOR]

Abdominal aortic aneurysm (AAA) is a common degenerative cardiovascular disease whose pathobiology is not clearly understood. The cellular heterogeneity and cell-type-specific gene regulation of vascular cells in human AAA have not been well-characterized. Here, we performed analysis of whole-genome sequencing data in AAA patients versus controls with the aim of detecting diseaseassociated variants that may affect gene regulation in human aortic smooth muscle cells (AoSMC) and human aortic endothelial cells (HAEC), two cell types of high relevance to AAA disease. To support this analysis, we generated H3K27ac HiChIP data for these cell types and inferred cell-type-specific gene regulatory networks. We observed that AAA-associated variants were most enriched in regulatory regions in AoSMC, compared with HAEC and CD4+ cells. The cell-type-specific regulation defined by this HiChIP data supported the importance of ERG and the KLF family of transcription factors in AAA disease. The analysis of regulatory elements that contain noncoding variants and also are differentially open between AAA patients and controls revealed the significance of the interleukin-6-mediated signaling pathway. This finding was further validated by including information from the deleteriousness effect of nonsynonymous single-nucleotide variants in AAA patients and additional control data from the Medical Genome Reference Bank dataset. These results shed important insights into AAA pathogenesis and provide a model for cell-type-specific analysis of disease-associated variants. [ABSTRACT FROM AUTHOR]

Background: Computed tomography-based evaluation of aortic stenosis (AS) by calcium scoring does not consider interleaflet differences in leaflet characteristics. Here, we sought to examine the functional implications of these differences. Methods: We retrospectively reviewed the computed tomography angiograms of 200 male patients with degenerative calcific AS undergoing transcatheter aortic valve replacement and 20 male patients with normal aortic valves. We compared the computed tomography angiography (CTA)-derived aortic valve leaflet calcification load (AVLCCTA), appearance, and systolic leaflet excursion (LEsys) of individual leaflets. We performed computer simulations of normal valves to investigate how interleaflet differences in LEsys affect aortic valve area. We used linear regression to identify predictors of leaflet-specific calcification in patients with AS. Results: In patients with AS, the noncoronary cusp (NCC) carried the greatest AVLCCTA (365.9 [237.3-595.4] Agatston unit), compared to the left coronary cusp (LCC, 278.5 [169.2-478.8] Agatston unit) and the right coronary cusp (RCC, 240.6 [137.3-439.0] Agatston unit; both P <0.001).>sys (42.8[degrees] [38.8[degrees]-49.0[degrees]]) compared to NCC (44.8[degrees] [41.1[degrees]-49.78[degrees]], P =0.001) and RCC (47.7[degrees] [42.0[degrees]-52.3[degrees]], P <0.001)> P <0.001).>CTA of NCC and RCC, but not LCC, predicted LEsys (both P <0.001)>CTA, RCC (P =0.005 and P =0.001). Conclusions: In male patients, the AVLCCTA of NCC and RCC contribute more to AS than that of LCC. LCC's propensity for noncalcific leaflet thickening and worse LEsys, however, should not be underestimated when using calcium scores to assess AS severity. [ABSTRACT FROM AUTHOR]

Biomedical studies have become larger in size and yielded large quantities of data, yet efficient data processing remains a challenge. Here we present Trellis, a cloud-based data and task management framework that completely automates the process from data ingestion to result presentation, while tracking data lineage, facilitating information query, and supporting fault-tolerance and scalability. Using a graph database to coordinate the state of the data processing workflows and a scalable microservice architecture to perform bioinformatics tasks, Trellis has enabled efficient variant calling on 100,000 human genomes collected in the VA Million Veteran Program. [ABSTRACT FROM AUTHOR]

Background: To describe an automated method for assessment of the plausibility of continuous variables collected in the electronic health record (EHR) data for real world evidence research use.Methods: The most widely used approach in quality assessment (QA) for continuous variables is to detect the implausible numbers using prespecified thresholds. In augmentation to the thresholding method, we developed a score-based method that leverages the longitudinal characteristics of EHR data for detection of the observations inconsistent with the history of a patient. The method was applied to the height and weight data in the EHR from the Million Veteran Program Data from the Veteran's Healthcare Administration (VHA). A validation study was also conducted.Results: The receiver operating characteristic (ROC) metrics of the developed method outperforms the widely used thresholding method. It is also demonstrated that different quality assessment methods have a non-ignorable impact on the body mass index (BMI) classification calculated from height and weight data in the VHA's database.Conclusions: The score-based method enables automated and scaled detection of the problematic data points in health care big data while allowing the investigators to select the high-quality data based on their need. Leveraging the longitudinal characteristics in EHR will significantly improve the QA performance. [ABSTRACT FROM AUTHOR]

Hereditary transthyretin-mediated (hATTR) amyloidosis is an underdiagnosed, progressively debilitating disease caused by mutations in the transthyretin (TTR) gene. V122I, a common pathogenic TTR mutation, is found in 3–4% of individuals of African ancestry in the United States and has been associated with cardiomyopathy and heart failure. To better understand the phenotypic consequences of carrying V122I, we conducted a phenome-wide association study scanning 427 ICD diagnosis codes in UK Biobank participants of African ancestry (n = 6062). Significant associations were tested for replication in the Penn Medicine Biobank (n = 5737) and the Million Veteran Program (n = 82,382). V122I was significantly associated with polyneuropathy in the UK Biobank (odds ratio [OR] = 6.4, 95% confidence interval [CI] 2.6–15.6, p = 4.2 × 10−5), which was replicated in the Penn Medicine Biobank (OR = 1.6, 95% CI 1.2–2.4, p = 6.0 × 10–3) and Million Veteran Program (OR = 1.5, 95% CI 1.2–1.8, p = 1.8 × 10−4). Polyneuropathy prevalence among V122I carriers was 2.1%, 9.0%, and 4.8% in the UK Biobank, Penn Medicine Biobank, and Million Veteran Program, respectively. The cumulative incidence of common hATTR amyloidosis manifestations (carpal tunnel syndrome, polyneuropathy, cardiomyopathy, heart failure) was significantly enriched in V122I carriers compared with non-carriers (HR = 2.8, 95% CI 1.7–4.5, p = 2.6 × 10−5) in the UK Biobank, with 37.4% of V122I carriers having at least one of these manifestations by age 75. Our findings show that V122I carriers are at increased risk of polyneuropathy. These results also emphasize the underdiagnosis of disease in V122I carriers with a significant proportion of subjects showing phenotypic changes consistent with hATTR amyloidosis. Greater understanding of the manifestations associated with V122I is critical for earlier diagnosis and treatment. [ABSTRACT FROM AUTHOR]