Search

Despite early interest, the evidence linking fatty acids to cardiovascular diseases remains controversial. We used Mendelian randomization to explore the involvement of polyunsaturated (PUFA) and monounsaturated (MUFA) fatty acids biosynthesis in the aetiology of several cardiovascular disease endpoints in up to 1,153,768 European and 212,453 East Asian ancestry individuals. As instruments, we selected single nucleotide polymorphisms (SNP) mapping to genes with well-known roles in PUFA (i.e. FADS1/2 and ELOVL2) and MUFA (i.e. SCD) biosynthesis. Our findings suggest that higher PUFA biosynthesis rate (proxied by rs174576 near FADS1/2) is related to higher odds of multiple cardiovascular diseases, particularly ischemic stroke, peripheral artery disease and venous thromboembolism, whereas higher MUFA biosynthesis rate (proxied by rs603424 near SCD) is related to lower odds of coronary artery disease among Europeans. Results were unclear for East Asians as most effect estimates were imprecise. By triangulating multiple approaches (i.e. uni-/multi-variable Mendelian randomization, a phenome-wide scan, genetic colocalization and within-sibling analyses), our results are compatible with higher low- density lipoprotein (LDL)-cholesterol (and possibly glucose) being a downstream effect of higher PUFA biosynthesis rate. Our findings indicate that genetically-determined PUFA and MUFA biosynthesis are involved in the aetiology of cardiovascular diseases and suggest LDL-cholesterol as a potential mediating trait between PUFA biosynthesis and cardiovascular diseases risk.

Background Machine learning (ML) is increasingly used in research for subtype definition and risk prediction, particularly in cardiovascular diseases. No existing ML models are routinely used for cardiovascular disease management, and their phase of clinical utility is unknown, partly due to a lack of clear criteria. We evaluated ML for subtype definition and risk prediction in heart failure (HF), acute coronary syndromes (ACS) and atrial fibrillation (AF). Methods For ML studies of subtype definition and risk prediction, we conducted a systematic review in HF, ACS and AF, using PubMed, MEDLINE and Web of Science from January 2000 until December 2019. By adapting published criteria for diagnostic and prognostic studies, we developed a seven-domain, ML-specific checklist. Results Of 5918 studies identified, 97 were included. Across studies for subtype definition (n = 40) and risk prediction (n = 57), there was variation in data source, population size (median 606 and median 6769), clinical setting (outpatient, inpatient, different departments), number of covariates (median 19 and median 48) and ML methods. All studies were single disease, most were North American (n = 61/97) and only 14 studies combined definition and risk prediction. Subtype definition and risk prediction studies respectively had limitations in development (e.g. 15.0% and 78.9% of studies related to patient benefit; 15.0% and 15.8% had low patient selection bias), validation (12.5% and 5.3% externally validated) and impact (32.5% and 91.2% improved outcome prediction; no effectiveness or cost-effectiveness evaluations). Conclusions Studies of ML in HF, ACS and AF are limited by number and type of included covariates, ML methods, population size, country, clinical setting and focus on single diseases, not overlap or multimorbidity. Clinical utility and implementation rely on improvements in development, validation and impact, facilitated by simple checklists. We provide clear steps prior to safe implementation of machine learning in clinical practice for cardiovascular diseases and other disease areas.

Heart regeneration requires multiple cell types to enable cardiomyocyte (CM) proliferation. How these cells interact to create growth niches is unclear. Here, we profile proliferation kinetics of cardiac endothelial cells (CECs) and CMs in the neonatal mouse heart and find that they are spatiotemporally coupled. We show that coupled myovascular expansion during cardiac growth or regeneration is dependent upon VEGF-VEGFR2 signaling, as genetic deletion of Vegfr2 from CECs or inhibition of VEGFA abrogates both CEC and CM proliferation. Repair of cryoinjury displays poor spatial coupling of CEC and CM proliferation. Boosting CEC density after cryoinjury with virus encoding Vegfa enhances regeneration. Using Mendelian randomization, we demonstrate that circulating VEGFA levels are positively linked with human myocardial mass, suggesting that Vegfa can stimulate human cardiac growth. Our work demonstrates the importance of coupled CEC and CM expansion and reveals a myovascular niche that may be therapeutically targeted for heart regeneration.

Individuals with South Asian ancestry have a higher risk of heart disease than other groups but have been largely excluded from genetic research. Using data from 22,000 British Pakistani and Bangladeshi individuals with linked electronic health records from the Genes & Health cohort, we conducted genome-wide association studies of coronary artery disease and its key risk factors. Using power-adjusted transferability ratios, we found evidence for transferability for the majority of cardiometabolic loci powered to replicate. The performance of polygenic scores was high for lipids and blood pressure, but lower for BMI and coronary artery disease. Adding a polygenic score for coronary artery disease to clinical risk factors showed significant improvement in reclassification. In Mendelian randomisation using transferable loci as instruments, our findings were consistent with results in European-ancestry individuals. Taken together, trait-specific transferability of trait loci between populations is an important consideration with implications for risk prediction and causal inference.

Objective Patient phenotype definitions based on terminologies are required for the computational use of electronic health records. Within UK primary care research databases, such definitions have typically been represented as flat lists of Read terms, but Systematized Nomenclature of Medicine—Clinical Terms (SNOMED CT) (a widely employed international reference terminology) enables the use of relationships between concepts, which could facilitate the phenotyping process. We implemented SNOMED CT-based phenotyping approaches and investigated their performance in the CPRD Aurum primary care database. Materials and Methods We developed SNOMED CT phenotype definitions for 3 exemplar diseases: diabetes mellitus, asthma, and heart failure, using 3 methods: “primary” (primary concept and its descendants), “extended” (primary concept, descendants, and additional relations), and “value set” (based on text searches of term descriptions). We also derived SNOMED CT codelists in a semiautomated manner for 276 disease phenotypes used in a study of health across the lifecourse. Cohorts selected using each codelist were compared to “gold standard” manually curated Read codelists in a sample of 500 000 patients from CPRD Aurum. Results SNOMED CT codelists selected a similar set of patients to Read, with F1 scores exceeding 0.93, and age and sex distributions were similar. The “value set” and “extended” codelists had slightly greater recall but lower precision than “primary” codelists. We were able to represent 257 of the 276 phenotypes by a single concept hierarchy, and for 135 phenotypes, the F1 score was greater than 0.9. Conclusions SNOMED CT provides an efficient way to define disease phenotypes, resulting in similar patient populations to manually curated codelists.

Diastole is the sequence of physiological events that occur in the heart during ventricular filling and principally depends on myocardial relaxation and chamber stiffness. Abnormal diastolic function is related to many cardiovascular disease processes and is predictive of health outcomes, but its genetic architecture is largely unknown. Here, we use machine-learning cardiac motion analysis to measure diastolic functional traits in 39,559 participants of the UK Biobank and perform a genome-wide association study. We identified nine significant, independent loci near genes that are associated with maintaining sarcomeric function under biomechanical stress and genes implicated in the development of cardiomyopathy. Age, sex and diabetes were independent predictors of diastolic function and we found a causal relationship between genetically determined ventricular stiffness and incident heart failure. Our results provide insights into the genetic and environmental factors influencing diastolic function that are relevant for identifying causal relationships and potential tractable targets.

Funder: Department of Health, Funder: Medical Research Council, BackgroundTargeted obesity prevention policies would benefit from the identification of population groups with the highest risk of weight gain. The relative importance of adult age, sex, ethnicity, geographical region, and degree of social deprivation on weight gain is not known. We aimed to identify high-risk groups for changes in weight and BMI using electronic health records (EHR).MethodsIn this longitudinal, population-based cohort study we used linked EHR data from 400 primary care practices (via the Clinical Practice Research Datalink) in England, accessed via the CALIBER programme. Eligible participants were aged 18-74 years, were registered at a general practice clinic, and had BMI and weight measurements recorded between Jan 1, 1998, and June 30, 2016, during the period when they had eligible linked data with at least 1 year of follow-up time. We calculated longitudinal changes in BMI over 1, 5, and 10 years, and investigated the absolute risk and odds ratios (ORs) of transitioning between BMI categories (underweight, normal weight, overweight, obesity class 1 and 2, and severe obesity [class 3]), as defined by WHO. The associations of demographic factors with BMI transitions were estimated by use of logistic regression analysis, adjusting for baseline BMI, family history of cardiovascular disease, use of diuretics, and prevalent chronic conditions.FindingsWe included 2 092 260 eligible individuals with more than 9 million BMI measurements in our study. Young adult age was the strongest risk factor for weight gain at 1, 5, and 10 years of follow-up. Compared with the oldest age group (65-74 years), adults in the youngest age group (18-24 years) had the highest OR (4·22 [95% CI 3·86-4·62]) and greatest absolute risk (37% vs 24%) of transitioning from normal weight to overweight or obesity at 10 years. Likewise, adults in the youngest age group with overweight or obesity at baseline were also at highest risk to transition to a higher BMI category; OR 4·60 (4·06-5·22) and absolute risk (42% vs 18%) of transitioning from overweight to class 1 and 2 obesity, and OR 5·87 (5·23-6·59) and absolute risk (22% vs 5%) of transitioning from class 1 and 2 obesity to class 3 obesity. Other demographic factors were consistently less strongly associated with these transitions; for example, the OR of transitioning from normal weight to overweight or obesity in people living in the most socially deprived versus least deprived areas was 1·23 (1·18-1·27), for men versus women was 1·12 (1·08-1·16), and for Black individuals versus White individuals was 1·13 (1·04-1·24). We provide an open access online risk calculator, and present high-resolution obesity risk charts over a 1-year, 5-year, and 10-year follow-up period.InterpretationA radical shift in policy is required to focus on individuals at the highest risk of weight gain (ie, young adults aged 18-24 years) for individual-level and population-level prevention of obesity and its long-term consequences for health and health care.FundingThe British Hearth Foundation, Health Data Research UK, the UK Medical Research Council, and the National Institute for Health Research.

Aims The HERMES (HEart failure Molecular Epidemiology for Therapeutic targets) consortium aims to identify the genomic and molecular basis of heart failure.Methods and results The consortium currently includes 51 studies from 11 countries, including 68 157 heart failure cases and 949 888 controls, with data on heart failure events and prognosis. All studies collected biological samples and performed genome-wide genotyping of common genetic variants. The enrolment of subjects into participating studies ranged from 1948 to the present day, and the median follow-up following heart failure diagnosis ranged from 2 to 116 months. Forty-nine of 51 individual studies enrolled participants of both sexes; in these studies, participants with heart failure were predominantly male (34-90%). The mean age at diagnosis or ascertainment across all studies ranged from 54 to 84 years. Based on the aggregate sample, we estimated 80% power to genetic variant associations with risk of heart failure with an odds ratio of >1.10 for common variants (allele frequency > 0.05) and >1.20 for low-frequency variants (allele frequency 0.01-0.05) at P < 5 x 10(-8) under an additive genetic model.Conclusions HERMES is a global collaboration aiming to (i) identify the genetic determinants of heart failure; (ii) generate insights into the causal pathways leading to heart failure and enable genetic approaches to target prioritization; and (iii) develop genomic tools for disease stratification and risk prediction.

Supplemental Digital Content is available in the text., Background: Cross-sectional measures of body mass index (BMI) are associated with cardiovascular disease (CVD) incidence, but less is known about whether weight change affects the risk of CVD. Methods: We estimated the effect of 2-y weight change interventions on 7-y risk of CVD (CVD death, myocardial infarction, stroke, hospitalization from coronary heart disease, and heart failure) by emulating hypothetical interventions using electronic health records. We identified 138,567 individuals with 45–69 years of age without chronic disease in England from 1998 to 2016. We performed pooled logistic regression, using inverse-probability weighting to adjust for baseline and time-varying confounders. We categorized each individual into a weight loss, maintenance, or gain group. Results: Among those of normal weight, both weight loss [risk difference (RD) vs. weight maintenance = 1.5% (0.3% to 3.0%)] and gain [RD = 1.3% (0.5% to 2.2%)] were associated with increased risk for CVD compared with weight maintenance. Among overweight individuals, we observed moderately higher risk of CVD in both the weight loss [RD = 0.7% (−0.2% to 1.7%)] and the weight gain group [RD = 0.7% (−0.1% to 1.7%)], compared with maintenance. In the obese, those losing weight showed lower risk of coronary heart disease [RD = −1.4% (−2.4% to −0.6%)] but not of stroke. When we assumed that chronic disease occurred 1–3 years before the recorded date, estimates for weight loss and gain were attenuated among overweight individuals; estimates for loss were lower among obese individuals. Conclusion: Among individuals with obesity, the weight-loss group had a lower risk of coronary heart disease but not of stroke. Weight gain was associated with increased risk of CVD across BMI groups. See video abstract at, http://links.lww.com/EDE/B838.

Objective The aim of the study was to transform a resource of linked electronic health records (EHR) to the OMOP common data model (CDM) and evaluate the process in terms of syntactic and semantic consistency and quality when implementing disease and risk factor phenotyping algorithms. Materials and Methods Using heart failure (HF) as an exemplar, we represented three national EHR sources (Clinical Practice Research Datalink, Hospital Episode Statistics Admitted Patient Care, Office for National Statistics) into the OMOP CDM 5.2. We compared the original and CDM HF patient population by calculating and presenting descriptive statistics of demographics, related comorbidities, and relevant clinical biomarkers. Results We identified a cohort of 502 536 patients with the incident and prevalent HF and converted 1 099 195 384 rows of data from 216 581 914 encounters across three EHR sources to the OMOP CDM. The largest percentage (65%) of unmapped events was related to medication prescriptions in primary care. The average coverage of source vocabularies was >98% with the exception of laboratory tests recorded in primary care. The raw and transformed data were similar in terms of demographics and comorbidities with the largest difference observed being 3.78% in the prevalence of chronic obstructive pulmonary disease (COPD). Conclusion Our study demonstrated that the OMOP CDM can successfully be applied to convert EHR linked across multiple healthcare settings and represent phenotyping algorithms spanning multiple sources. Similar to previous research, challenges mapping primary care prescriptions and laboratory measurements still persist and require further work. The use of OMOP CDM in national UK EHR is a valuable research tool that can enable large-scale reproducible observational research.

OBJECTIVE The aim of this study was to use Mendelian randomization (MR) techniques to estimate the causal relationships between genetic liability to type 2 diabetes (T2D), glycemic traits, and risk of heart failure (HF). RESEARCH DESIGN AND METHODS Summary-level data were obtained from genome-wide association studies of T2D, insulin resistance (IR), glycated hemoglobin, fasting insulin and glucose, and HF. MR was conducted using the inverse-variance weighted method. Sensitivity analyses included the MR-Egger method, weighted median and mode methods, and multivariable MR conditioning on potential mediators. RESULTS Genetic liability to T2D was causally related to higher risk of HF (odds ratio [OR] 1.13 per 1-log unit higher risk of T2D; 95% CI 1.11–1.14; P < 0.001); however, sensitivity analysis revealed evidence of directional pleiotropy. The relationship between T2D and HF was attenuated when adjusted for coronary disease, BMI, LDL cholesterol, and blood pressure in multivariable MR. Genetically instrumented higher IR was associated with higher risk of HF (OR 1.19 per 1-log unit higher risk of IR; 95% CI 1.00–1.41; P = 0.041). There were no notable associations identified between fasting insulin, glucose, or glycated hemoglobin and risk of HF. Genetic liability to HF was causally linked to higher risk of T2D (OR 1.49; 95% CI 1.01–2.19; P = 0.042), although again with evidence of pleiotropy. CONCLUSIONS These findings suggest a possible causal role of T2D and IR in HF etiology, although the presence of both bidirectional effects and directional pleiotropy highlights potential sources of bias that must be considered.

Background: Each of the cardiomyopathies, classically categorized as hypertrophic cardiomyopathy, dilated cardiomyopathy (DCM), and arrhythmogenic right ventricular cardiomyopathy, has a signature genetic theme. Hypertrophic cardiomyopathy and arrhythmogenic right ventricular cardiomyopathy are largely understood as genetic diseases of sarcomere or desmosome proteins, respectively. In contrast, >250 genes spanning >10 gene ontologies have been implicated in DCM, representing a complex and diverse genetic architecture. To clarify this, a systematic curation of evidence to establish the relationship of genes with DCM was conducted. Methods: An international panel with clinical and scientific expertise in DCM genetics evaluated evidence supporting monogenic relationships of genes with idiopathic DCM. The panel used the Clinical Genome Resource semiquantitative gene-disease clinical validity classification framework with modifications for DCM genetics to classify genes into categories on the basis of the strength of currently available evidence. Representation of DCM genes on clinically available genetic testing panels was evaluated. Results: Fifty-one genes with human genetic evidence were curated. Twelve genes (23%) from 8 gene ontologies were classified as having definitive ( BAG3 , DES , FLNC , LMNA , MYH7 , PLN , RBM20 , SCN5A , TNNC1 , TNNT2 , TTN ) or strong ( DSP ) evidence. Seven genes (14%; ACTC1 , ACTN2 , JPH2 , NEXN , TNNI3 , TPM1 , VCL ) including 2 additional ontologies were classified as moderate evidence; these genes are likely to emerge as strong or definitive with additional evidence. Of these 19 genes, 6 were similarly classified for hypertrophic cardiomyopathy and 3 for arrhythmogenic right ventricular cardiomyopathy. Of the remaining 32 genes (63%), 25 (49%) had limited evidence, 4 (8%) were disputed, 2 (4%) had no disease relationship, and 1 (2%) was supported by animal model data only. Of the 16 evaluated clinical genetic testing panels, most definitive genes were included, but panels also included numerous genes with minimal human evidence. Conclusions: In the curation of 51 genes, 19 had high evidence (12 definitive/strong, 7 moderate). It is notable that these 19 genes explain only a minority of cases, leaving the remainder of DCM genetic architecture incompletely addressed. Clinical genetic testing panels include most high-evidence genes; however, genes lacking robust evidence are also commonly included. We recommend that high-evidence DCM genes be used for clinical practice and that caution be exercised in the interpretation of variants in variable-evidence DCM genes.

Background: It is known that certain cardiovascular diseases (CVD) are associated, like atrial fibrillation and stroke. However, for other CVDs, the links and temporal trends are less studied. In this longitudinal study, we have investigated temporal epidemiological and genetic associations between different CVDs. Methods: The ULSAM (Uppsala Longitudinal Study of Adult Men; 2322 men aged 50 years) has been followed for 40 years regarding 4 major CVDs (incident myocardial infarction, ischemic stroke, heart failure, and atrial fibrillation). For the genetic analyses, publicly available data were used. Results: Using multistate modeling, significant relationships were seen between pairs of all of the 4 investigated CVDs. However, the risk of obtaining one additional CVD differed substantially both between different CVDs and between their temporal order. The relationship between heart failure and atrial fibrillation showed a high risk ratio (risk ratios, 24–26) regardless of the temporal order. A consistent association was seen also for myocardial infarction and atrial fibrillation but with a lower relative risk (risk ratios, 4–5). In contrast, the risk of receiving a diagnosis of heart failure following a myocardial infarction was almost twice as high as for the reverse temporal order (risk ratios, 16 versus 9). Genetic loci linked to traditional risk factors could partly explain the observed associations between the CVDs, but pathway analyses disclosed also other pathophysiological links. Conclusions: During 40 years, all of the 4 investigated CVDs were pairwise associated with each other regardless of the temporal order of occurrence, but the risk magnitude differed between different CVDs and their temporal order. Genetic analyses disclosed new pathophysiological links between CVDs.

The heart muscle diseases hypertrophic (HCM) and dilated (DCM) cardiomyopathies are leading causes of sudden death and heart failure in young, otherwise healthy, individuals. We conducted genome-wide association studies and multi-trait analyses in HCM (1,733 cases), DCM (5,521 cases) and nine left ventricular (LV) traits (19,260 UK Biobank participants with structurally normal hearts). We identified 16 loci associated with HCM, 13 with DCM and 23 with LV traits. We show strong genetic correlations between LV traits and cardiomyopathies, with opposing effects in HCM and DCM. Two-sample Mendelian randomization supports a causal association linking increased LV contractility with HCM risk. A polygenic risk score explains a significant portion of phenotypic variability in carriers of HCM-causing rare variants. Our findings thus provide evidence that polygenic risk score may account for variability in Mendelian diseases. More broadly, we provide insights into how genetic pathways may lead to distinct disorders through opposing genetic effects.

Reducing the burden of late-life morbidity requires an understanding of the mechanisms of ageing-related diseases (ARDs), defined as diseases that accumulate with increasing age. This has been hampered by the lack of formal criteria to identify ARDs. Here, we present a framework to identify ARDs using two complementary methods consisting of unsupervised machine learning and actuarial techniques, which we applied to electronic health records (EHRs) from 3,009,048 individuals in England using primary care data from the Clinical Practice Research Datalink (CPRD) linked to the Hospital Episode Statistics admitted patient care dataset between 1 April 2010 and 31 March 2015 (mean age 49.7 years (s.d. 18.6), 51% female, 70% white ethnicity). We grouped 278 high-burden diseases into nine main clusters according to their patterns of disease onset, using a hierarchical agglomerative clustering algorithm. Four of these clusters, encompassing 207 diseases spanning diverse organ systems and clinical specialties, had rates of disease onset that clearly increased with chronological age. However, the ages of onset for these four clusters were strikingly different, with median age of onset 82 years (IQR 82–83) for Cluster 1, 77 years (IQR 75–77) for Cluster 2, 69 years (IQR 66–71) for Cluster 3 and 57 years (IQR 54–59) for Cluster 4. Fitting to ageing-related actuarial models confirmed that the vast majority of these 207 diseases had a high probability of being ageing-related. Cardiovascular diseases and cancers were highly represented, while benign neoplastic, skin and psychiatric conditions were largely absent from the four ageing-related clusters. Our framework identifies and clusters ARDs and can form the basis for fundamental and translational research into ageing pathways.

Background: The cardiomyopathies are classically categorized as hypertrophic (HCM), dilated (DCM), and arrhythmogenic right ventricular (ARVC), and each have a signature genetic theme. HCM and ARVC are largely understood as genetic diseases of sarcomere or desmosome proteins, respectively. In contrast, >250 genes spanning more than 10 gene ontologies have been implicated in DCM, representing a complex and diverse genetic architecture. To clarify this, a systematic curation of evidence to establish the relationship of genes with DCM was conducted. Methods: An international Panel with clinical and scientific expertise in DCM genetics was assembled to evaluate evidence supporting monogenic relationships of genes with idiopathic DCM. The Panel utilized the ClinGen semi-quantitative gene-disease clinical validity classification framework. Results: Fifty-one genes with human genetic evidence were curated. Twelve genes (23%) from eight gene ontologies were classified as having definitive (BAG3, DES, FLNC, LMNA, MYH7, PLN, RBM20, SCN5A, TNNC1, TNNT2, TTN) or strong (DSP) evidence. Seven genes (14%) (ACTC1, ACTN2, JPH2, NEXN, TNNI3, TPM1, VCL) including two additional ontologies were classified as moderate evidence; these genes are likely to emerge as strong or definitive with additional evidence. Of the 19 genes classified as definitive, strong or moderate, six were similarly classified for HCM and three for ARVC. Of the remaining 32 genes (63%), 25 (49%) had limited evidence, 4 (8%) were disputed, 2 (4%) had no disease relationship, and 1 (2%) was supported by animal model data only. Of 16 commercially available genetic testing panels evaluated, most definitive genes were included, but panels also included numerous genes with minimal human evidence. Conclusions: In a systematic curation of published evidence for genes considered relevant for monogenic DCM, 12 were classified as definitive or strong and seven as moderate evidence spanning 10 gene ontologies. Notably, these 19 genes only explain a minority of DCM cases, leaving the remainder of DCM genetic architecture incompletely addressed. While clinical genetic testing panels include most high evidence genes, genes lacking robust evidence are also commonly included. Until the genetic architecture of DCM is more fully defined, care should be taken in the interpretation of variable evidence DCM genes in clinical practice.

Background H3K27ac histone acetylome changes contribute to the phenotypic response in heart diseases, particularly in end-stage heart failure. However, such epigenetic alterations have not been systematically investigated in remodeled non-failing human hearts. Therefore, valuable insight into cardiac dysfunction in early remodeling is lacking. This study aimed to reveal the acetylation changes of chromatin regions in response to myocardial remodeling and their correlations to transcriptional changes of neighboring genes. Results We detected chromatin regions with differential acetylation activity (DARs; Padj. < 0.05) between remodeled non-failing patient hearts and healthy donor hearts. The acetylation level of the chromatin region correlated with its RNA polymerase II occupancy level and the mRNA expression level of its adjacent gene per sample. Annotated genes from DARs were enriched in disease-related pathways, including fibrosis and cell metabolism regulation. DARs that change in the same direction have a tendency to cluster together, suggesting the well-reorganized chromatin architecture that facilitates the interactions of regulatory domains in response to myocardial remodeling. We further show the differences between the acetylation level and the mRNA expression level of cell-type-specific markers for cardiomyocytes and 11 non-myocyte cell types. Notably, we identified transcriptome factor (TF) binding motifs that were enriched in DARs and defined TFs that were predicted to bind to these motifs. We further showed 64 genes coding for these TFs that were differentially expressed in remodeled myocardium when compared with controls. Conclusions Our study reveals extensive novel insight on myocardial remodeling at the DNA regulatory level. Differences between the acetylation level and the transcriptional level of cell-type-specific markers suggest additional mechanism(s) between acetylome and transcriptome. By integrating these two layers of epigenetic profiles, we further provide promising TF-encoding genes that could serve as master regulators of myocardial remodeling. Combined, our findings highlight the important role of chromatin regulatory signatures in understanding disease etiology.

The inner surfaces of the human heart are covered by a complex network of muscular strands that is thought to be a remnant of embryonic development1,2. The function of these trabeculae in adults and their genetic architecture are unknown. Here we performed a genome-wide association study to investigate image-derived phenotypes of trabeculae using the fractal analysis of trabecular morphology in 18,096 participants of the UK Biobank. We identified 16 significant loci that contain genes associated with haemodynamic phenotypes and regulation of cytoskeletal arborization3,4. Using biomechanical simulations and observational data from human participants, we demonstrate that trabecular morphology is an important determinant of cardiac performance. Through genetic association studies with cardiac disease phenotypes and Mendelian randomization, we find a causal relationship between trabecular morphology and risk of cardiovascular disease. These findings suggest a previously unknown role for myocardial trabeculae in the function of the adult heart, identify conserved pathways that regulate structural complexity and reveal the influence of the myocardial trabeculae on susceptibility to cardiovascular disease. A genome-wide association study shows that myocardial trabeculae are an important determinant of cardiac performance in the adult heart, identifies conserved pathways that regulate structural complexity and reveals the influence of trabeculae on the susceptibility to cardiovascular disease.

Approximately half of all patients with heart failure have preserved left ventricular ejection fraction (LVEF), yet in contrast to heart failure with reduced ejection fraction (HFrEF), no treatments have yet been shown to be effective at improving outcomes.1 Neurohumoral activation is observed in heart failure across the full spectrum of left ventricular function2; however, pharmacological inhibition of the renin–angiotensin aldosterone system (RAAS) has only been shown to improve survival and reduce hospitalisation in HFrEF.1 We therefore performed a Cochrane systematic review and meta-analysis to synthesize all available evidence to investigate the effects of RAAS inhibition in heart failure patients with a LVEF >40%. This patient population comprises both heart failure with mid-range ejection fraction (40%–49%) and heart failure with preserved ejection fraction (HFpEF, >50%), as defined in current guidelines1. We searched for randomised controlled trials (RCTs) investigating pharmacological neurohumoral inhibition with beta-blockers (BB), ACE inhibitors (ACEI), angiotensin receptor blockers (ARB), mineralocorticoid receptor antagonists (MRA) and angiotensin receptor–neprilysin inhibitors, in adult patients (≥ 18 years old) with heart failure and LVEF >40%. We searched MEDLINE, Embase and the Cochrane Central Register of Controlled Trials on 25 July 2017. The primary outcomes were cardiovascular (CV) mortality, heart failure hospitalisation and hyperkalaemia; and the secondary outcomes were all-cause mortality, quality of life (measured using either the Minnesota Living with Heart Failure (MLHF) Questionnaire or Kansas City Cardiomyopathy Questionnaire ). We undertook meta-analysis for each drug class and outcome. when data were available from more than one clinical trial. We used a fixed-effect model unless heterogeneity was high (I²≥50%) when a random-effect model was used. We assessed the quality of evidence for each comparison using the GRADE ( Grading of Recommendations, Assessment, Development and Evaluations ) methodology. The review was conducted in accordance with the guidelines provided in the Cochrane Handbook,3 and reported findings …

The αvβ6 integrin is involved in the pathogenesis of cancer and fibrosis. A radiolabeled 20-amino-acid αvβ6-binding peptide, derived from the foot and mouth virus (NAVPNLRGDLQVLAQKVART [A20FMDV2]), has been developed to image αvβ6 levels preclinically. This study was designed to translate these findings into a clinical PET imaging protocol to measure the expression of αvβ6 in humans. Methods: Preclinical toxicology was undertaken, and a direct immunoassay was developed for 4-fluorobenzamide (FB)-A20FMDV2. Four healthy human subjects (2 male and 2 female) received a single microdose of 18F-FB-A20FMDV2 followed by a multibed PET scan of the whole body over more than 3 h. Results: There were no findings in the preclinical toxicology assessments, and no anti-A20FMDV2 antibodies were detected before or after dosing with the PET ligand. The mean and SD of the administered mass of 18F-FB-A20FMDV2 was 8.7 ± 4.4 μg (range, 2.7-13.0 μg). The mean administered activity was 124 ± 20 MBq (range, 98-145 MBq). There were no adverse or clinically detectable pharmacologic effects in any of the subjects. No significant changes in vital signs, laboratory study results, or electrocardiography results were observed. Uptake of radioactivity was observed in the thyroid, salivary glands, liver, stomach wall, spleen, kidneys, ureters, and bladder. Time-activity curves indicated that the highest activity was in the bladder content, followed by the kidneys, small intestine, stomach, liver, spleen, thyroid, and gallbladder. The largest component of the residence times was the voided urine, followed by muscle, bladder, and liver. Using the mean residence time over all subjects as input to OLINDA/EXM, the effective dose was determined to be 0.0217 mSv/MBq; using residence times from single subjects gave an SD of 0.0020 mSv/MBq from the mean. The critical organ was the urinary bladder, with an absorbed dose of 0.18 mGy/MBq. Conclusion:18F-FB-A20FMDV2 successfully passed toxicology criteria, showed no adverse effects in this first-in-humans study, and has an effective dose that enables multiple scans in a single subject.

BackgroundNaturally occurring human genetic variants provide a valuable tool to identify drug targets and guide drug prioritization and clinical trial design. Ivabradine is a heart rate lowering drug with protective effects on heart failure despite increasing the risk of atrial fibrillation. In patients with coronary artery disease without heart failure, the drug does not protect against major cardiovascular adverse events prompting questions about the ability of genetics to have predicted those effects. This study evaluates the effect of a mutation in HCN4, ivabradine’s drug target, on safety and efficacy endpoints.MethodsWe used genetic association testing and Mendelian randomization to predict the effect of ivabradine and heart rate lowering on cardiovascular outcomes.ResultsUsing data from the UK Biobank and large GWAS consortia, we evaluated the effect of a heart rate-reducing genetic variant at the HCN4 locus encoding ivabradine’s drug target. These genetic association analyses showed increases in risk for atrial fibrillation (OR 1.09, 95% CI: 1.06-1.13, P=9.3 ×10−9) in the UK Biobank. In a cause-specific competing risk model to account for the increased risk of atrial fibrillation, the HCN4 variant reduced incident heart failure in participants that did not develop atrial fibrillation (HR 0.90, 95% CI: 0.83-0.98, P=0.013). In contrast, the same heart rate reducing HCN4 variant did not prevent a composite endpoint of myocardial infarction or cardiovascular death (OR 0.99, 95% CI: 0.93-1.04, P=0.61).ConclusionGenetic modelling of ivabradine recapitulates its benefits in heart failure, promotion of atrial fibrillation, and neutral effect on myocardial infarction.CONDENSED ABSTRACTThe effects of drugs can sometimes be predicted from the effects of mutations in genes encoding drug targets. We tested the effect of a heart rate reducing allele at the HCN4 locus encoding ivabradine’s drug target and found results coherent with the SHIFT and SIGNIFY clinical trials of ivabradine. The genetic variant increased the risk of atrial fibrillation and cardioembolic stroke and protected against heart failure in a competing risk model accounting for the increased risk of atrial fibrillation. The variant had a neutral effect on a composite of myocardial infarction and cardiovascular death.

Additional file 4 Supplementary Figure 4. The mRNA expression level of cell-type-specific markers. Genes labeled red and blue were significantly up- and downregulated in patients versus controls (p < 0.05).

Additional file 13. Supplementary Table 7. Overview of included samples in H3K27ac ChIP-seq, RNAPII ChIP-seq, and RNA-seq experiments.

Additional file 5. Supplementary Figure 5. Gene Ontology (GO) analysis of genes annotated to DARs using ToppFun. a Molecular function enrichment for hyperacetylated regions, b Biological process for hyperacetylated regions, c Cellular component for hyperacetylated regions, d Molecular function for hypoacetylated regions, e Biological process for hypoacetylated regions, f Cellular component for hypoacetylated regions.

Summary: Background: To effectively prevent, detect, and treat health conditions that affect people during their lifecourse, health-care professionals and researchers need to know which sections of the population are susceptible to which health conditions and at which ages. Hence, we aimed to map the course of human health by identifying the 50 most common health conditions in each decade of life and estimating the median age at first diagnosis. Methods: We developed phenotyping algorithms and codelists for physical and mental health conditions that involve intensive use of health-care resources. Individuals older than 1 year were included in the study if their primary-care and hospital-admission records met research standards set by the Clinical Practice Research Datalink and they had been registered in a general practice in England contributing up-to-standard data for at least 1 year during the study period. We used linked records of individuals from the CALIBER platform to calculate the sex-standardised cumulative incidence for these conditions by 10-year age groups between April 1, 2010, and March 31, 2015. We also derived the median age at diagnosis and prevalence estimates stratified by age, sex, and ethnicity (black, white, south Asian) over the study period from the primary-care and secondary-care records of patients. Findings: We developed case definitions for 308 disease phenotypes. We used records of 2 784 138 patients for the calculation of cumulative incidence and of 3 872 451 patients for the calculation of period prevalence and median age at diagnosis of these conditions. Conditions that first gained prominence at key stages of life were: atopic conditions and infections that led to hospital admission in children (

Background Beta-blockers and inhibitors of the renin-angiotensin-aldosterone system improve survival and reduce morbidity in people with heart failure with reduced left ventricular ejection fraction (LVEF); a review of the evidence is required to determine whether these treatments are beneficial for people with heart failure with preserved ejection fraction (HFpEF). Objectives To assess the effects of beta-blockers, angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, angiotensin receptor neprilysin inhibitors, and mineralocorticoid receptor antagonists in people with HFpEF. Search methods We updated searches of CENTRAL, MEDLINE, Embase, and one clinical trial register on 14 May 2020 to identify eligible studies, with no language or date restrictions. We checked references from trial reports and review articles for additional studies. SELECTION CRITERIA: We included randomised controlled trials with a parallel group design, enrolling adults with HFpEF, defined by LVEF greater than 40%. Data collection and analysis We used standard methodological procedures expected by Cochrane. Main results We included 41 randomised controlled trials (231 reports), totalling 23,492 participants across all comparisons. The risk of bias was frequently unclear and only five studies had a low risk of bias in all domains. Beta-blockers (BBs) We included 10 studies (3087 participants) investigating BBs. Five studies used a placebo comparator and in five the comparator was usual care. The mean age of participants ranged from 30 years to 81 years. A possible reduction in cardiovascular mortality was observed (risk ratio (RR) 0.78, 95% confidence interval (CI) 0.62 to 0.99; number needed to treat for an additional benefit (NNTB) 25; 1046 participants; three studies), however, the certainty of evidence was low. There may be little to no effect on all-cause mortality (RR 0.82, 95% CI 0.67 to 1.00; 1105 participants; four studies; low-certainty evidence). The effects on heart failure hospitalisation, hyperkalaemia, and quality of life remain uncertain. Mineralocorticoid receptor antagonists (MRAs) We included 13 studies (4459 participants) investigating MRA. Eight studies used a placebo comparator and in five the comparator was usual care. The mean age of participants ranged from 54.5 to 80 years. Pooled analysis indicated that MRA treatment probably reduces heart failure hospitalisation (RR 0.82, 95% CI 0.69 to 0.98; NNTB = 41; 3714 participants; three studies; moderate-certainty evidence). However, MRA treatment probably has little or no effect on all-cause mortality (RR 0.91, 95% CI 0.78 to 1.06; 4207 participants; five studies; moderate-certainty evidence) and cardiovascular mortality (RR 0.90, 95% CI 0.74 to 1.11; 4070 participants; three studies; moderate-certainty evidence). MRA treatment may have little or no effect on quality of life measures (mean difference (MD) 0.84, 95% CI -2.30 to 3.98; 511 participants; three studies; low-certainty evidence). MRA treatment was associated with a higher risk of hyperkalaemia (RR 2.11, 95% CI 1.77 to 2.51; number needed to treat for an additional harmful outcome (NNTH) = 11; 4291 participants; six studies; high-certainty evidence). Angiotensin-converting enzyme inhibitors (ACEIs) We included eight studies (2061 participants) investigating ACEIs. Three studies used a placebo comparator and in five the comparator was usual care. The mean age of participants ranged from 70 to 82 years. Pooled analyses with moderate-certainty evidence suggest that ACEI treatment likely has little or no effect on cardiovascular mortality (RR 0.93, 95% CI 0.61 to 1.42; 945 participants; two studies), all-cause mortality (RR 1.04, 95% CI 0.75 to 1.45; 1187 participants; five studies) and heart failure hospitalisation (RR 0.86, 95% CI 0.64 to 1.15; 1019 participants; three studies), and may result in little or no effect on the quality of life (MD -0.09, 95% CI -3.66 to 3.48; 154 participants; two studies; low-certainty evidence). The effects on hyperkalaemia remain uncertain. Angiotensin receptor blockers (ARBs) Eight studies (8755 participants) investigating ARBs were included. Five studies used a placebo comparator and in three the comparator was usual care. The mean age of participants ranged from 61 to 75 years. Pooled analyses with high certainty of evidence suggest that ARB treatment has little or no effect on cardiovascular mortality (RR 1.02, 95% 0.90 to 1.14; 7254 participants; three studies), all-cause mortality (RR 1.01, 95% CI 0.92 to 1.11; 7964 participants; four studies), heart failure hospitalisation (RR 0.92, 95% CI 0.83 to 1.02; 7254 participants; three studies), and quality of life (MD 0.41, 95% CI -0.86 to 1.67; 3117 participants; three studies). ARB was associated with a higher risk of hyperkalaemia (RR 1.88, 95% CI 1.07 to 3.33; 7148 participants; two studies; high-certainty evidence). Angiotensin receptor neprilysin inhibitors (ARNIs) Three studies (7702 participants) investigating ARNIs were included. Two studies used ARBs as the comparator and one used standardised medical therapy, based on participants' established treatments at enrolment. The mean age of participants ranged from 71 to 73 years. Results suggest that ARNIs may have little or no effect on cardiovascular mortality (RR 0.96, 95% CI 0.79 to 1.15; 4796 participants; one study; moderate-certainty evidence), all-cause mortality (RR 0.97, 95% CI 0.84 to 1.11; 7663 participants; three studies; high-certainty evidence), or quality of life (high-certainty evidence). However, ARNI treatment may result in a slight reduction in heart failure hospitalisation, compared to usual care (RR 0.89, 95% CI 0.80 to 1.00; 7362 participants; two studies; moderate-certainty evidence). ARNI treatment was associated with a reduced risk of hyperkalaemia compared with valsartan (RR 0.88, 95% CI 0.77 to 1.01; 5054 participants; two studies; moderate-certainty evidence). Authors' conclusions There is evidence that MRA and ARNI treatment in HFpEF probably reduces heart failure hospitalisation but probably has little or no effect on cardiovascular mortality and quality of life. BB treatment may reduce the risk of cardiovascular mortality, however, further trials are needed. The current evidence for BBs, ACEIs, and ARBs is limited and does not support their use in HFpEF in the absence of an alternative indication. Although MRAs and ARNIs are probably effective at reducing the risk of heart failure hospitalisation, the treatment effect sizes are modest. There is a need for improved approaches to patient stratification to identify the subgroup of patients who are most likely to benefit from MRAs and ARNIs, as well as for an improved understanding of disease biology, and for new therapeutic approaches.

The α

This is a protocol for a Cochrane Review (Intervention). The objectives are as follows: To evaluate the safety and efficacy of beta‐blockers (BBs), angiotensin converting enzyme inhibitors (ACEIs), angiotensin receptor antagonists (ARBs), mineralocorticoid receptor antagonists (MRAs), and angiotensin receptor neprilysin inhibitors (ARNIs) for the treatment of patients with heart failure (HF) with preserved ejection fraction (HFpEF).

Target identification (identifying the correct drug targets for each disease) and target validation (demonstrating the effect of target perturbation on disease biomarkers and disease end-points) are essential steps in drug development. We showed previously that biomarker and disease endpoint associations of single nucleotide polymorphisms (SNPs) in a gene encoding a drug target accurately depict the effect of modifying the same target with a pharmacological agent; others have shown that genomic support for a target is associated with a higher rate of drug development success. To delineate drug development (including repurposing) opportunities arising from this paradigm, we connected complex disease- and biomarker-associated loci from genome wide association studies (GWAS) to an updated set of genes encoding druggable human proteins, to compounds with bioactivity against these targets and, where these were licensed drugs, to clinical indications. We used this set of genes to inform the design of a new genotyping array, to enable druggable genome-wide association studies for drug target selection and validation in human disease.

Cardiac progenitor/stem cells in adult hearts represent an attractive therapeutic target for heart regeneration, though (inter)-relationships among reported cells remain obscure. Using single-cell qRT–PCR and clonal analyses, here we define four subpopulations of cardiac progenitor/stem cells in adult mouse myocardium all sharing stem cell antigen-1 (Sca1), based on side population (SP) phenotype, PECAM-1 (CD31) and platelet-derived growth factor receptor-α (PDGFRα) expression. SP status predicts clonogenicity and cardiogenic gene expression (Gata4/6, Hand2 and Tbx5/20), properties segregating more specifically to PDGFRα+ cells. Clonal progeny of single Sca1+ SP cells show cardiomyocyte, endothelial and smooth muscle lineage potential after cardiac grafting, augmenting cardiac function although durable engraftment is rare. PDGFRα− cells are characterized by Kdr/Flk1, Cdh5, CD31 and lack of clonogenicity. PDGFRα+/CD31− cells derive from cells formerly expressing Mesp1, Nkx2-5, Isl1, Gata5 and Wt1, distinct from PDGFRα−/CD31+ cells (Gata5 low; Flk1 and Tie2 high). Thus, PDGFRα demarcates the clonogenic cardiogenic Sca1+ stem/progenitor cell., Adult cardiac progenitor/stem cells (CPSCs) possess valuable potential for heart repair that is limited by the elusiveness of these cells. Here Noseda et al. refine the definition of murine CPSCs producing stem cell antigen 1 (Sca1), mapping the cardiogenic signature and clonogenicity to the subgroup of Sca1+ cells expressing PDGFRα.

Big data is important to new developments in global clinical science that aim to improve the lives of patients. Technological advances have led to the regular use of structured electronic health-care records with the potential to address key deficits in clinical evidence that could improve patient care. The COVID-19 pandemic has shown this potential in big data and related analytics but has also revealed important limitations. Data verification, data validation, data privacy, and a mandate from the public to conduct research are important challenges to effective use of routine health-care data. The European Society of Cardiology and the BigData@Heart consortium have brought together a range of international stakeholders, including representation from patients, clinicians, scientists, regulators, journal editors, and industry members. In this Review, we propose the CODE-EHR minimum standards framework to be used by researchers and clinicians to improve the design of studies and enhance transparency of study methods. The CODE-EHR framework aims to develop robust and effective utilisation of health-care data for research purposes.

Additional file 2 Figure S1. PRISMA flow diagram.

Additional file 6 Web Table 3. Subtype classification studies in other disease areas.

Additional file 1. Search terms and search strategy.

Additional file 1. Search terms and search strategy.

Additional file 6 Web Table 3. Subtype classification studies in other disease areas.

Additional file 2 Figure S1. PRISMA flow diagram.

Additional file 5. Methods and Results for scoping review.

Additional file 9. PRISMA checklist.

Additional file 7 Web Table 4. Risk prediction studies in other disease areas.

Additional file 9. PRISMA checklist.