In the past decade many Genome-wide Association Studies (GWAS) were performed that discovered new associations between single-nucleotide polymorphisms (SNPs) and various phenotypes. Imputation ...methods are widely used in GWAS. They facilitate the phenotype association with variants that are not directly genotyped. Imputation methods can also be used to combine and analyse data genotyped on different genotyping arrays. In this study we investigated the imputation quality and efficiency of two different approaches of combining GWAS data from different genotyping platforms. We investigated whether combining data from different platforms before the actual imputation performs better than combining the data from different platforms after imputation.
In total 979 unique individuals from the AMC-PAS cohort were genotyped on 3 different platforms. A total of 706 individuals were genotyped on the MetaboChip, a total of 757 individuals were genotyped on the 50K gene-centric Human CVD BeadChip, and a total of 955 individuals were genotyped on the HumanExome chip. A total of 397 individuals were genotyped on all 3 individual platforms. After pre-imputation quality control (QC), Minimac in combination with MaCH was used for the imputation of all samples with the 1,000 genomes reference panel. All imputed markers with an r2 value of <0.3 were excluded in our post-imputation QC.
A total of 397 individuals were genotyped on all three platforms. All three datasets were carefully matched on strand, SNP ID and genomic coordinates. This resulted in a dataset of 979 unique individuals and a total of 258,925 unique markers. A total of 4,117,036 SNPs were available when imputation was performed before merging the three datasets. A total of 3,933,494 SNPs were available when imputation was done on the combined set. Our results suggest that imputation of individual datasets before merging performs slightly better than after combining the different datasets.
Imputation of datasets genotyped by different platforms before merging generates more SNPs than imputation after putting the datasets together.
Observational studies have demonstrated that type 2 diabetes is a stronger risk factor for coronary heart disease (CHD) in women compared with men. However, it is not clear whether this reflects a ...sex differential in the causal effect of diabetes on CHD risk or results from sex-specific residual confounding.
Using 270 single nucleotide polymorphisms (SNPs) for type 2 diabetes identified in a type 2 diabetes genome-wide association study, we performed a sex-stratified Mendelian randomization (MR) study of type 2 diabetes and CHD using individual participant data in UK Biobank (251,420 women and 212,049 men). Weighted median, MR-Egger, MR-pleiotropy residual sum and outlier, and radial MR from summary-level analyses were used for pleiotropy assessment.
MR analyses showed that genetic risk of type 2 diabetes increased the odds of CHD for women (odds ratio 1.13 95% CI 1.08-1.18 per 1-log unit increase in odds of type 2 diabetes) and men (1.21 1.17-1.26 per 1-log unit increase in odds of type 2 diabetes). Sensitivity analyses showed some evidence of directional pleiotropy; however, results were similar after correction for outlier SNPs.
This MR analysis supports a causal effect of genetic liability to type 2 diabetes on risk of CHD that is not stronger for women than men. Assuming a lack of bias, these findings suggest that the prevention and management of type 2 diabetes for CHD risk reduction is of equal priority in both sexes.
Highlights • Cancer treatment can have long-term detrimental cardiovascular side-effects. • Awareness of these long-term side-effects is of crucial value in the management of these patients. • ...Prospective information is needed to fill the current gaps in knowledge (in particular regarding the long-term side-effects of non-anthracyclines). • Multidisciplinary efforts will reduce the impact of the side-effects of anticancer treatments.
Background
Late right heart failure (LRHF) is a common complication during long‐term left ventricular assist device (LVAD) support. We aimed to identify risk factors for LRHF after LVAD implantation.
...Methods
Patients undergoing primary LVAD implantation between 2006 and 2019 and surviving the perioperative period were included for this study (n = 261). Univariate Cox proportional hazards analysis was used to assess the association of clinical covariates and LRHF, stratified for device type. Variables with p < 0.10 entered the multivariable model. In a subset of patients with complete echocardiography or right catheterization data, this multivariable model was extended. Postoperative cardiopulmonary exercise test data were compared in patients with and without LRHF.
Results
Nineteen percentage of patients suffered from LRHF after a median of 12 months, of which 67% required hospitalization. A history of atrial fibrillation (AF) (HR: 2.06 1.08–3.93, p = 0.029), a higher preoperative body mass index (BMI) (HR: 1.07 1.01–1.13, p = 0.023), and intensive care unit (ICU) duration (HR: 1.03 1.00–1.06, p = 0.025) were independent predictors of LHRF in the multivariable model. A significant relation between the severity of tricuspid regurgitation (TR) and LRHF (HR: 1.91 1.13–3.21, p = 0.016) was found in patients with echocardiographic data. Patients with LRHF demonstrated a lower maximal workload and peak VO2 at 6 months postoperatively.
Conclusion
A history of AF, BMI, and longer ICU stay may help identify patients at high risk for LRHF. Severity of TR was significantly related to LRHF in a subset of patients
The aim, methods, and results of this study, where risk factors for late right heart failure in long‐term mechanical circulatory support were analyzed
Key points
Mutations in genes encoding cardiac troponin I (TNNI3) and cardiac troponin T (TNNT2) caused altered troponin protein stoichiometry in patients with dilated cardiomyopathy.
TNNI3p.98trunc ...resulted in haploinsufficiency, increased Ca2+‐sensitivity and reduced length‐dependent activation.
TNNT2p.K217del caused increased passive tension.
A mutation in the gene encoding Lamin A/C (LMNAp.R331Q) led to reduced maximal force development through secondary disease remodelling in patients suffering from dilated cardiomyopathy.
Our study shows that different gene mutations induce dilated cardiomyopathy via diverse cellular pathways.
Dilated cardiomyopathy (DCM) can be caused by mutations in sarcomeric and non‐sarcomeric genes. In this study we defined the pathogenic effects of three DCM‐causing mutations: the sarcomeric mutations in genes encoding cardiac troponin I (TNNI3p.98truncation) and cardiac troponin T (TNNT2p.K217deletion; also known as the p.K210del) and the non‐sarcomeric gene mutation encoding lamin A/C (LMNAp.R331Q). We assessed sarcomeric protein expression and phosphorylation and contractile behaviour in single membrane‐permeabilized cardiomyocytes in human left ventricular heart tissue. Exchange with recombinant troponin complex was used to establish the direct pathogenic effects of the mutations in TNNI3 and TNNT2. The TNNI3p.98trunc and TNNT2p.K217del mutation showed reduced expression of troponin I to 39% and 51%, troponin T to 64% and 53%, and troponin C to 73% and 97% of controls, respectively, and altered stoichiometry between the three cardiac troponin subunits. The TNNI3p.98trunc showed pure haploinsufficiency, increased Ca2+‐sensitivity and impaired length‐dependent activation. The TNNT2p.K217del mutation showed a significant increase in passive tension that was not due to changes in titin isoform composition or phosphorylation. Exchange with wild‐type troponin complex corrected troponin protein levels to 83% of controls in the TNNI3p.98trunc sample. Moreover, upon exchange all functional deficits in the TNNI3p.98trunc and TNNT2p.K217del samples were normalized to control values confirming the pathogenic effects of the troponin mutations. The LMNAp.R331Q mutation resulted in reduced maximal force development due to disease remodelling. Our study shows that different gene mutations induce DCM via diverse cellular pathways.
Key points
Mutations in genes encoding cardiac troponin I (TNNI3) and cardiac troponin T (TNNT2) caused altered troponin protein stoichiometry in patients with dilated cardiomyopathy.
TNNI3p.98trunc resulted in haploinsufficiency, increased Ca2+‐sensitivity and reduced length‐dependent activation.
TNNT2p.K217del caused increased passive tension.
A mutation in the gene encoding Lamin A/C (LMNAp.R331Q) led to reduced maximal force development through secondary disease remodelling in patients suffering from dilated cardiomyopathy.
Our study shows that different gene mutations induce dilated cardiomyopathy via diverse cellular pathways.
The medical field has seen a rapid increase in the development of artificial intelligence (AI)-based prediction models. With the introduction of such AI-based prediction model tools and software in ...cardiovascular patient care, the cardiovascular researcher and healthcare professional are challenged to understand the opportunities as well as the limitations of the AI-based predictions. In this article, we present 12 critical questions for cardiovascular health professionals to ask when confronted with an AI-based prediction model. We aim to support medical professionals to distinguish the AI-based prediction models that can add value to patient care from the AI that does not.
Abstract
Aims
Cohorts of millions of people's health records, whole genome sequencing, imaging, sensor, societal and publicly available data present a rapidly expanding digital trace of health. We ...aimed to critically review, for the first time, the challenges and potential of big data across early and late stages of translational cardiovascular disease research.
Methods and results
We sought exemplars based on literature reviews and expertise across the BigData@Heart Consortium. We identified formidable challenges including: data quality, knowing what data exist, the legal and ethical framework for their use, data sharing, building and maintaining public trust, developing standards for defining disease, developing tools for scalable, replicable science and equipping the clinical and scientific work force with new inter-disciplinary skills. Opportunities claimed for big health record data include: richer profiles of health and disease from birth to death and from the molecular to the societal scale; accelerated understanding of disease causation and progression, discovery of new mechanisms and treatment-relevant disease sub-phenotypes, understanding health and diseases in whole populations and whole health systems and returning actionable feedback loops to improve (and potentially disrupt) existing models of research and care, with greater efficiency. In early translational research we identified exemplars including: discovery of fundamental biological processes e.g. linking exome sequences to lifelong electronic health records (EHR) (e.g. human knockout experiments); drug development: genomic approaches to drug target validation; precision medicine: e.g. DNA integrated into hospital EHR for pre-emptive pharmacogenomics. In late translational research we identified exemplars including: learning health systems with outcome trials integrated into clinical care; citizen driven health with 24/7 multi-parameter patient monitoring to improve outcomes and population-based linkages of multiple EHR sources for higher resolution clinical epidemiology and public health.
Conclusion
High volumes of inherently diverse (‘big’) EHR data are beginning to disrupt the nature of cardiovascular research and care. Such big data have the potential to improve our understanding of disease causation and classification relevant for early translation and to contribute actionable analytics to improve health and healthcare.
Peripartum cardiomyopathy (PPCM) and dilated cardiomyopathy (DCM) show similarities in clinical presentation. However, although DCM patients do not recover and slowly deteriorate further, PPCM ...patients show either a fast cardiac deterioration or complete recovery. The aim of this study was to assess if underlying cellular changes can explain the clinical similarities and differences in the two diseases. We, therefore, assessed sarcomeric protein expression, modification, titin isoform shift, and contractile behavior of cardiomyocytes in heart tissue of PPCM and DCM patients and compared these with nonfailing controls. Heart samples from ischemic heart disease (ISHD) patients served as heart failure control samples. Passive force was only increased in PPCM samples compared with controls, whereas PPCM, DCM, and ISHD samples all showed increased myofilament Ca2+ sensitivity. Length-dependent activation was significantly impaired in PPCM compared with controls, no impairment was observed in ISHD samples, and DCM samples showed an intermediate response. Contractile impairments were caused by impaired protein kinase A (PKA)–mediated phosphorylation because exogenous PKA restored all parameters to control levels. Although DCM samples showed reexpression of EH-myomesin, an isoform usually only expressed in the heart before birth, PPCM and ISHD did not. The lack of EH-myomesin, combined with low PKA-mediated phosphorylation of myofilament proteins and increased compliant titin isoform, may explain the increase in passive force and blunted length-dependent activation of myofilaments in PPCM samples.
Using a systematic approach, a collection of expressions for the series resistance of a solar cell are derived from the diode model. Many published series resistance determination methods are among ...them, or are slight variations on them. Some expressions have not yet been described in the literature. Representation of the methods in a two-dimensional array allows for easy comparison and reveals that many of the previously published methods are more alike than might appear at first sight. From a discussion of the various methods, on the basis of the two-dimensional array arrangement, an overview of the required approximations and assumptions for each method is assembled. Taking the effect of these approximations and assumptions into account, it is expected that the method of Wolf & Rauschenbach will provide the most accurate value for the series resistance of a solar cell.
•PV cell series resistance expressions are systematically derived from the diode model.•These can be translated into series resistance determination methods.•Among them are many published methods.•An overview of the used approximations and assumptions for each method is presented.•The method of Wolf & Rauschenbach provides the most accurate series resistance value.
Clinical manifestations and outcomes of atherosclerotic disease differ between ethnic groups. In addition, the prevalence of risk factors is substantially different. Primary prevention programs are ...based on data derived from almost exclusively White people. We investigated how race/ethnic differences modify the associations of established risk factors with atherosclerosis and cardiovascular events.
We used data from an ongoing individual participant meta-analysis involving 17 population-based cohorts worldwide. We selected 60,211 participants without cardiovascular disease at baseline with available data on ethnicity (White, Black, Asian or Hispanic). We generated a multivariable linear regression model containing risk factors and ethnicity predicting mean common carotid intima-media thickness (CIMT) and a multivariable Cox regression model predicting myocardial infarction or stroke. For each risk factor we assessed how the association with the preclinical and clinical measures of cardiovascular atherosclerotic disease was affected by ethnicity.
Ethnicity appeared to significantly modify the associations between risk factors and CIMT and cardiovascular events. The association between age and CIMT was weaker in Blacks and Hispanics. Systolic blood pressure associated more strongly with CIMT in Asians. HDL cholesterol and smoking associated less with CIMT in Blacks. Furthermore, the association of age and total cholesterol levels with the occurrence of cardiovascular events differed between Blacks and Whites.
The magnitude of associations between risk factors and the presence of atherosclerotic disease differs between race/ethnic groups. These subtle, yet significant differences provide insight in the etiology of cardiovascular disease among race/ethnic groups. These insights aid the race/ethnic-specific implementation of primary prevention.
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