The discovery of the endothelium as a crucial organ for the regulation of the vasculature to physiological needs and the recognition of endothelial dysfunction as a key pathological condition - which is associated with most if not all cardiovascular risk factors - led to a tremendous boost of endothelial research in the past 3 decades. Despite the possibility to measure endothelial function in the individual and its widespread use in research, its use as a clinical tool in daily medicine is not established yet. We review the most common methods to assess vascular function in humans and discuss their advantages and disadvantages. Furthermore we give an overview about clinical settings were endothelial function measurements may be valuable in individual patients. Specifically, we provide information why endothelial function is not only a risk marker for cardiovascular risk but may also provides prognostic information beyond commonly used risk scores in primary prevention, and in patients with already established coronary disease. We conclude, that non-invasive endothelial function measurements provide valuable additional information, however, to ascertain its use for daily clinical practice, future research should determine whether endothelial function can be used to guide treatment in the individual and if this translates into better outcomes.

Genetic variants are the primary driver of congenital heart disease (CHD) pathogenesis. However, our ability to identify causative variants is limited. To identify causal CHD genes that are associated with specific molecular functions, the study used prior knowledge to filter de novo variants from 2,881 probands with sporadic severe CHD. This approach enabled the authors to identify an association between left ventricular outflow tract obstruction lesions and genes associated with the WAVE2 complex and regulation of small GTPase-mediated signal transduction. Using CRISPR zebrafish knockdowns, the study confirmed that WAVE2 complex proteins brk1, nckap1, and wasf2 and the regulators of small GTPase signaling cul3a and racgap1 are critical to cardiac development.

Congenital heart disease (CHD) is the most frequent birth defect, affecting 0.8% of live births. Many cases occur sporadically and impair reproductive fitness, suggesting a role for de novo mutations. Here we compare the incidence of de novo mutations in 362 severe CHD cases and 264 controls by analysing exome sequencing of parent-offspring trios. CHD cases show a significant excess of protein-altering de novo mutations in genes expressed in the developing heart, with an odds ratio of 7.5 for damaging (premature termination, frameshift, splice site) mutations. Similar odds ratios are seen across the main classes of severe CHD. We find a marked excess of de novo mutations in genes involved in the production, removal or reading of histone 3 lysine 4 (H3K4) methylation, or ubiquitination of H2BK120, which is required for H3K4 methylation. There are also two de novo mutations in SMAD2, which regulates H3K27 methylation in the embryonic left-right organizer. The combination of both activating (H3K4 methylation) and inactivating (H3K27 methylation) chromatin marks characterizes poised promoters and enhancers, which regulate expression of key developmental genes. These findings implicate de novo point mutations in several hundreds of genes that collectively contribute to approximately 10% of severe CHD.

Congenital heart disease (CHD) patients have an increased prevalence of extracardiac congenital anomalies (CAs) and risk of neurodevelopmental disabilities (NDDs). Exome sequencing of 1213 CHD parent-offspring trios identified an excess of protein-damaging de novo mutations, especially in genes highly expressed in the developing heart and brain. These mutations accounted for 20% of patients with CHD, NDD, and CA but only 2% of patients with isolated CHD. Mutations altered genes involved in morphogenesis, chromatin modification, and transcriptional regulation, including multiple mutations in RBFOX2, a regulator of mRNA splicing. Genes mutated in other cohorts examined for NDD were enriched in CHD cases, particularly those with coexisting NDD. These findings reveal shared genetic contributions to CHD, NDD, and CA and provide opportunities for improved prognostic assessment and early therapeutic intervention in CHD patients.

Congenital heart disease (CHD) is the leading cause of mortality from birth defects. Here, exome sequencing of a single cohort of 2,871 CHD probands, including 2,645 parent-offspring trios, implicated rare inherited mutations in 1.8%, including a recessive founder mutation in GDF1 accounting for ∼5% of severe CHD in Ashkenazim, recessive genotypes in MYH6 accounting for ∼11% of Shone complex, and dominant FLT4 mutations accounting for 2.3% of Tetralogy of Fallot. De novo mutations (DNMs) accounted for 8% of cases, including ∼3% of isolated CHD patients and ∼28% with both neurodevelopmental and extra-cardiac congenital anomalies. Seven genes surpassed thresholds for genome-wide significance, and 12 genes not previously implicated in CHD had >70% probability of being disease related. DNMs in ∼440 genes were inferred to contribute to CHD. Striking overlap between genes with damaging DNMs in probands with CHD and autism was also found.

Carotid artery intima media thickness (cIMT) and carotid plaque are measures of subclinical atherosclerosis associated with ischemic stroke and coronary heart disease (CHD). Here, we undertake meta-analyses of genome-wide association studies (GWAS) in 71,128 individuals for cIMT, and 48,434 individuals for carotid plaque traits. We identify eight novel susceptibility loci for cIMT, one independent association at the previously-identified PINX1 locus, and one novel locus for carotid plaque. Colocalization analysis with nearby vascular expression quantitative loci (cis-eQTLs) derived from arterial wall and metabolic tissues obtained from patients with CHD identifies candidate genes at two potentially additional loci, ADAMTS9 and LOXL4. LD score regression reveals significant genetic correlations between cIMT and plaque traits, and both cIMT and plaque with CHD, any stroke subtype and ischemic stroke. Our study provides insights into genes and tissue-specific regulatory mechanisms linking atherosclerosis both to its functional genomic origins and its clinical consequences in humans.

Objective

To evaluate the extent to which the inter-institutional, inter-disciplinary mobilisation of data and skills in the Farr Institute contributed to establishing the emerging field of data science for health in the UK.

Design and outcome measures

We evaluated evidence of six domains characterising a new field of science:defining central scientific challenges,demonstrating how the central challenges might be solved,creating novel interactions among groups of scientists,training new types of experts,re-organising universities,demonstrating impacts in society.We carried out citation, network and time trend analyses of publications, and a narrative review of infrastructure, methods and tools.

Setting

Four UK centres in London, North England, Scotland and Wales (23 university partners), 2013-2018.

Results

1. The Farr Institute helped define a central scientific challenge publishing a research corpus, demonstrating insights from electronic health record (EHR) and administrative data at each stage of the translational cycle in 593 papers with at least one Farr Institute author affiliation on PubMed. 2. The Farr Institute offered some demonstrations of how these scientific challenges might be solved: it established the first four ISO27001 certified trusted research environments in the UK, and approved more than 1000 research users, published on 102 unique EHR and administrative data sources, although there was no clear evidence of an increase in novel, sustained record linkages. The Farr Institute established open platforms for the EHR phenotyping algorithms and validations (>70 diseases, CALIBER). Sample sizes showed some evidence of increase but remained less than 10% of the UK population in primary care-hospital care linked studies. 3.The Farr Institute created novel interactions among researchers: the co-author publication network expanded from 944 unique co-authors (based on 67 publications in the first 30 months) to 3839 unique co-authors (545 papers in the final 30 months). 4. Training expanded substantially with 3 new masters courses, training >400 people at masters, short-course and leadership level and 48 PhD students. 5. Universities reorganised with 4/5 Centres established 27 new faculty (tenured) positions, 3 new university institutes. 6. Emerging evidence of impacts included: > 3200 citations for the 10 most cited papers and Farr research informed eight practice-changing clinical guidelines and policies relevant to the health of millions of UK citizens.

Conclusion

The Farr Institute played a major role in establishing and growing the field of data science for health in the UK, with some initial evidence of benefits for health and healthcare. The Farr Institute has now expanded into Health Data Research (HDR) UK but key challenges remain including, how to network such activities internationally.

Identification of sequence variants robustly associated with predisposition to diabetic kidney disease (DKD) has the potential to provide insights into the pathophysiological mechanisms responsible. We conducted a genome-wide association study (GWAS) of DKD in type 2 diabetes (T2D) using eight complementary dichotomous and quantitative DKD phenotypes: the principal dichotomous analysis involved 5,717 T2D subjects, 3,345 with DKD. Promising association signals were evaluated in up to 26,827 subjects with T2D (12,710 with DKD). A combined T1D+T2D GWAS was performed using complementary data available for subjects with T1D, which, with replication samples, involved up to 40,340 subjects with diabetes (18,582 with DKD). Analysis of specific DKD phenotypes identified a novel signal near GABRR1 (rs9942471, P = 4.5 × 10^-8) associated with microalbuminuria in European T2D case subjects. However, no replication of this signal was observed in Asian subjects with T2D or in the equivalent T1D analysis. There was only limited support, in this substantially enlarged analysis, for association at previously reported DKD signals, except for those at UMOD and PRKAG2, both associated with estimated glomerular filtration rate. We conclude that, despite challenges in addressing phenotypic heterogeneity, access to increased sample sizes will continue to provide more robust inference regarding risk variant discovery for DKD.