Background:The extent to which COVID-19 diagnosis and vaccination during pregnancy are associated with risks of common and rare adverse pregnancy outcomes remains uncertain. We compared the incidence of adverse pregnancy outcomes in women with and without COVID-19 diagnosis and vaccination during pregnancy. Methods:We studied population-scale linked electronic health records for women with singleton pregnancies in England and Wales from 1 August 2019 to 31 December 2021. This time period was divided at 8th December 2020 into pre-vaccination and vaccination roll-out eras. We calculated adjusted hazard ratios (HRs) for common and rare pregnancy outcomes according to the time since COVID-19 diagnosis and vaccination and by pregnancy trimester and COVID-19 variant. Findings:Amongst 865,654 pregnant women, we recorded 60,134 (7%) COVID-19 diagnoses and 182,120 (21%) adverse pregnancy outcomes. COVID-19 diagnosis was associated with a higher risk of gestational diabetes (adjusted HR 1.22, 95% CI 1.18-1.26), gestational hypertension (1.16, 1.10-1.22), pre-eclampsia (1.20, 1.12-1.28), preterm birth (1.63, 1.57-1.69, and 1.68, 1.61-1.75 for spontaneous preterm), very preterm birth (2.04, 1.86-2.23), small for gestational age (1.12, 1.07-1.18), thrombotic venous events (1.85, 1.56-2.20) and stillbirth (only within 14-days since COVID-19 diagnosis, 3.39, 2.23-5.15). HRs were more pronounced in the pre-vaccination era, within 14-days since COVID-19 diagnosis, when COVID-19 diagnosis occurred in the 3rd trimester, and in the original variant era. There was no evidence to suggest COVID-19 vaccination during pregnancy was associated with a higher risk of adverse pregnancy outcomes. Instead, dose 1 of COVID-19 vaccine was associated with lower risks of preterm birth (0.90, 0.86-0.95), very preterm birth (0.84, 0.76-0.94), small for gestational age (0.93, 0.88-0.99), and stillbirth (0.67, 0.49-0.92). Interpretation:Pregnant women with a COVID-19 diagnosis have higher risks of adverse pregnancy outcomes. These findings support recommendations towards high-priority vaccination against COVID-19 in pregnant women. Funding:BHF, ESRC, Forte, HDR-UK, MRC, NIHR and VR.

Objective The aim of this study is to understand stakeholder experiences of diagnosis of cardiovascular disease (CVD) to support the development of technological solutions that meet current needs. Specifically, we aimed to identify challenges in the process of diagnosing CVD, to identify discrepancies between patient and clinician experiences of CVD diagnosis, and to identify the requirements of future health technology solutions intended to improve CVD diagnosis.Design Semistructured focus groups and one-to-one interviews to generate qualitative data that were subjected to thematic analysis.Participants UK-based individuals (N=32) with lived experience of diagnosis of CVD (n=23) and clinicians with experience in diagnosing CVD (n=9).Results We identified four key themes related to delayed or inaccurate diagnosis of CVD: symptom interpretation, patient characteristics, patient–clinician interactions and systemic challenges. Subthemes from each are discussed in depth. Challenges related to time and communication were greatest for both stakeholder groups; however, there were differences in other areas, for example, patient experiences highlighted difficulties with the psychological aspects of diagnosis and interpreting ambiguous symptoms, while clinicians emphasised the role of individual patient differences and the lack of rapport in contributing to delays or inaccurate diagnosis.Conclusions Our findings highlight key considerations when developing digital technologies that seek to improve the efficiency and accuracy of diagnosis of CVD.

Local haemodynamics control arterial homeostasis and dysfunction by generating wall shear stress (WSS) which regulates endothelial cell (EC) physiology. Here we use a zebrafish model to identify genes that regulate EC proliferation in response to flow. Suppression of blood flow in zebrafish embryos (by targeting cardiac troponin) reduced EC proliferation in the intersegmental vessels (ISVs) compared to controls exposed to flow. The expression of candidate regulators of proliferation was analysed in EC isolated from zebrafish embryos by qRT-PCR. Genes shown to be expressed in EC were analysed for the ability to regulate proliferation in zebrafish vasculature exposed to flow or no-flow conditions using a knockdown approach. wnk1 negatively regulated proliferation in no-flow conditions, whereas fzd5, gsk3β, trpm7 and bmp2a promoted proliferation in EC exposed to flow. Immunofluorescent staining of mammalian arteries revealed that WNK1 is expressed at sites of low WSS in the murine aorta, and in EC overlying human atherosclerotic plaques. We conclude that WNK1 is expressed in EC at sites of low WSS and in diseased arteries and may influence vascular homeostasis by reducing EC proliferation.

Longitudinal patient registries generate important evidence for advancing clinical care and the regulatory evaluation of health-care products. Most national registries rely on data collected as part of routine clinical encounters, an approach that does not capture real-world, patient-centred outcomes, such as physical activity, fatigue, ability to do daily tasks, and other indicators of quality of life. Digital health technologies that obtain such real-world data could greatly enhance patient registries but unresolved challenges have so far prevented their broad adoption. Based on our experience implementing digital health technologies in registries and observational studies, we propose potential solutions to three practical challenges we have repeatedly encountered: determining what to measure digitally, selecting the appropriate device, and ensuring representativeness and engagement over time. We describe the example of a hypothetical patient registry for valvular heart disease, a condition for which there is substantial variation in treatment selection and postintervention outcomes, and for which patient-centred outcome data are urgently needed to inform clinical care guidelines and health-service commissioning.

Background: The extent to which COVID-19 diagnosis and vaccination during pregnancy stages are associated with risks of common and rare adverse pregnancy outcomes remains uncertain.Methods: We studied population-scale linked electronic health records for women with singleton pregnancies in England and Wales from 1 August 2019 to 31 December 2021. This time period was further divided at 8th December 2020 into pre-vaccination and vaccination roll-out eras. We calculated adjusted hazard ratios (HRs) for common and rare pregnancy outcomes according to the time since COVID-19 diagnosis and vaccination and by pregnancy trimester. Findings: Amongst 865,654 pregnant women, we recorded 60,134 (7%) COVID-19 diagnoses and 182,120 (21%) cardiovascular-related adverse pregnancy outcomes. COVID-19 diagnosis was associated with a higher risk of gestational diabetes (adjusted HR 1.22, 95% CI 1.18-1.26), gestational hypertension (1.16, 1.10-1.22), pre-eclampsia (1.20, 1.12-1.28), preterm birth (1.63, 1.57-1.69, and 1.68, 1.61-1.75 for spontaneous preterm), very preterm birth (2.04, 1.86-2.23), small for gestational age (1.12, 1.07-1.18), thrombotic venous events (1.85, 1.56-2.20) and stillbirth (only within 14-days since COVID-19 diagnosis, HR 3.39, 2.23-5.15). HRs were more pronounced in the pre-vaccination era, within 14-days since COVID-19 diagnosis, and when COVID-19 diagnosis occurred in the 3rd trimester. There was no evidence to suggest COVID-19 vaccination during pregnancy was associated with a higher risk of adverse pregnancy outcomes.Interpretation: Pregnant women with a COVID-19 diagnosis have higher risks of adverse pregnancy outcomes. These findings support recommendations towards high-priority vaccination against COVID-19 in pregnant women.Funding: The British Heart Foundation Data Science Centre (grant No SP/19/3/34678, awarded to Health Data Research (HDR) UK) funded co-development (with NHS England) of the Secure Data Environment service for England, provision of linked datasets, data access, user software licences, computational usage, and data management and wrangling support, with additional contributions from the HDR UK Data and Connectivity component of the UK Government Chief Scientific Adviser’s National Core Studies programme to coordinate national COVID-19 priority research. This work was supported by the COVID-19 Longitudinal Health and Wellbeing National Core Study, which is funded by the Medical Research Council (MC_PC_20059) and by the CONVALSCENCE study, which is funded by NIHR (COV-LT-0009). This work was supported by the Con-COV team funded by the Medical Research Council (grant number: MR/V028367/1). This work was supported by Health Data Research UK, which receives its funding from HDR UK Ltd (HDR-9006) funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation (BHF) and the Wellcome Trust. This work was supported by the ADR Wales programme of work. This research has been supported by the ADR Wales programme of work. ADR Wales, part of the ADR UK investment, unites research expertise from Swansea University Medical School and WISERD (Wales Institute of Social and Economic Research and Data) at Cardiff University with analysts from Welsh Government. ADR UK is funded by the Economic and Social Research Council (ESRC), part of UK Research and Innovation. This research was supported by ESRC funding, including Administrative Data Research Wales (ES/W012227/1). Consortium partner organisations funded the time of contributing data analysts, biostatisticians, epidemiologists, and clinicians. This research was supported by the National Institute for Health and Care Research (NIHR) Cambridge Biomedical Research Centre (BRC-1215-20014; NIHR203312). ER was supported by HDR-UK2022.0173; ‘Developing capacity and capability to undertake UK-wide studies on >65M people using COVID-19 as an exemplar (COALESCE)’ and by The Swedish Research Council for Health, Working Life and Welfare (Forte 2022-00882). LZ’s contribution to this study is supported by Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant ref MC_PC_20058), with additional support by The Alan Turing Institute via ‘Towards Turing 2.0’ EPSRC Grant Funding and by the Health Data Science Centre, Human Technopole, Milan (Italy). RD’s contribution to this study is supported by NIHR Bristol Biomedical Research and Health Data Research UK South-West. DAL’s contribution to this study is supported by the British Heart Foundation (CH/F/20/90003 and AA/18/1/34219) and the UK Medical Research Council (MC_UU_00032/05). AMW is part of the BigData@Heart Consortium, funded by the Innovative Medicines Initiative-2 Joint Undertaking under grant agreement No 116074.Declaration of Interest: G Smith: GSK. Consultant and member of expert panel for RSV vaccination in pregnancy. GSK. Member of Data Safety Monitoring Committee for trials of RSV vaccination in pregnancy Moderna. Member of Data Safety Monitoring Committee for trials of RSV vaccination in pregnancy. No other conflicts of interest to be disclosed.Ethical Approval: The North East - Newcastle and North Tyneside 2 research ethics committee provided ethical approval for the CVD-COVID-UK research program (REC no. 20/NE/0161) to access, within secure trusted research environments, unconsented, whole-population, de-identified data from EHRs collected as part of patients’ routine healthcare.

For more than 60 years, humans have travelled into space. Until now, the majority of astronauts have been professional, government agency astronauts selected, in part, for their superlative physical fitness and the absence of disease. Commercial spaceflight is now becoming accessible to members of the public, many of whom would previously have been excluded owing to unsatisfactory fitness or the presence of cardiorespiratory diseases. While data exist on the effects of gravitational and acceleration (G) forces on human physiology, data on the effects of the aerospace environment in unselected members of the public, and particularly in those with clinically significant pathology, are limited. Although short in duration, these high acceleration forces can potentially either impair the experience or, more seriously, pose a risk to health in some individuals. Rather than expose individuals with existing pathology to G forces to collect data, computational modelling might be useful to predict the nature and severity of cardiovascular diseases that are of sufficient risk to restrict access, require modification, or suggest further investigation or training before flight. In this Review, we explore state-of-the-art, zero-dimensional, compartmentalized models of human cardiovascular pathophysiology that can be used to simulate the effects of acceleration forces, homeostatic regulation and ventilation-perfusion matching, using data generated by long-arm centrifuge facilities of the US National Aeronautics and Space Administration and the European Space Agency to risk stratify individuals and help to improve safety in commercial suborbital spaceflight. During commercial spaceflight, individuals who might have underlying cardiovascular disease will be exposed to increased gravitational and acceleration (G) forces. In this Review, Morris and colleagues explore the use of computational models to simulate the effects of G forces on human cardiovascular pathophysiology to risk-stratify individuals and help to improve safety in commercial suborbital spaceflight. Commercial suborbital spaceflight (CSOS) is a relatively new enterprise that, unlike traditional aviation, involves considerable acceleration (G) forces, which represents a new challenge for operators and aviation authorities who are required to develop medical safety guidelines.Physiologically, CSOS participants are unlike professional astronauts, and the effects of increased G forces in these individuals are largely unknown, particularly in those with ageing or diseased cardiovascular systems.Computational modelling is being used to simulate the effects of CSOS on the human cardiovascular system in various diseased and comorbid states to assess tolerance to increased G and to help to develop an evidence base to support medical guideline development.Zero-dimensional, electrical analogue models are apposite for this objective, with a fully transient analysis and representation of homeostatic regulation mechanisms, species (oxygen and carbon dioxide) transport and directional G loading, which can be represented with capacitors.Challenges include how clinical data are integrated and interpreted in the model and how model outputs will be validated, because exposing potentially high-risk individuals to extreme G forces might be unsafe and, therefore, unethical.

Physical activity and cardiovascular disease (CVD) are intimately linked. Low levels of physical activity increase the risk of CVDs, including myocardial infarction and stroke. Conversely, when CVD develops, it often reduces the ability to be physically active. Despite these largely understood relationships, the objective measurement of physical activity is rarely performed in routine healthcare. The ability to use sensor-based approaches to accurately measure aspects of physical activity has the potential to improve many aspects of cardiovascular healthcare across the spectrum of healthcare, from prediction, prevention, diagnosis, and treatment to disease monitoring. This review discusses the potential of sensor-based measurement of physical activity to augment current cardiovascular healthcare. We highlight many factors that should be considered to maximise the benefit and reduce the risks of such an approach. Because the widespread use of such devices in society is already a reality, it is important that scientists, clinicians, and healthcare providers are aware of these considerations.

Stepping—encompassing walking, running and stairclimbing—is the fundamental mode of human movement. Higher stepping volume and intensity is associated with favourable health outcomes. 2 Over the last quarter of the century, stepping has declined by over 1000 steps per day (7%–13% of total count, roughly equivalent to ~10 min of brisk walking). As a simple ‘objective’ measure of ambulatory physical activity, formal steppingbased recommendations may provide a target that is easy to understand and monitor. As selfmonitoring of steps may be an effective physical activity intervention, such recommendations may support more people to be sufficiently active. This editorial discusses the opportunities and challenges surrounding the addition of steppingbased recommendations to future guidelines. DAILY STEPS: AN OLD-NEW TARGET? Current physical activity recommendations are based on weekly duration (time) of moderate and vigorous activity (MVPA). For some people steps may be an easier to monitor and more concrete behavioural metric than time at a particular intensity. For example, step counting devices (pedometers, accelerometers or smartphones) have historically been more accessible than MVPA timequantifying devices. Simple mechanical pedometers first appeared almost 60 years ago around the Tokyo 1964 Olympics, with the Yamasa companydesigned ‘ManpoKei’ (‘10 000 steps metre’) being the first commercial step counter. The proliferation of stepcounting devices in the last 20 years saw the 10 000 daily steps target being treated as an unofficial goal that increasingly attracted public attention (online supplemental image 1).

The past decade has seen a dramatic rise in consumer technologies able to monitor a variety of cardiovascular parameters. Such devices initially recorded markers of exercise, but now include physiological and health-care focused measurements. The public are keen to adopt these devices in the belief that they are useful to identify and monitor cardiovascular disease. Clinicians are therefore often presented with health app data accompanied by a diverse range of concerns and queries. Herein, we assess whether these devices are accurate, their outputs validated, and whether they are suitable for professionals to make management decisions. We review underpinning methods and technologies and explore the evidence supporting the use of these devices as diagnostic and monitoring tools in hypertension, arrhythmia, heart failure, coronary artery disease, pulmonary hypertension, and valvular heart disease. Used correctly, they might improve health care and support research.

Low physical activity increases the risk of cardiovascular disease (CVD).

Hereditary haemorrhagic telangiectasia (HHT) causes arteriovenous malformations (AVMs) in multiple organs to cause bleeding, neurological and other complications. HHT is caused by mutations in the BMP co-receptor endoglin. We characterised a range of vascular phenotypes in embryonic and adult endoglin mutant zebrafish and the effect of inhibiting different pathways downstream of Vegf signalling. Adult endoglin mutant zebrafish developed skin AVMs, retinal vascular abnormalities and cardiac enlargement. Embryonic endoglin mutants developed an enlarged basilar artery (similar to the previously described enlarged aorta and cardinal vein) and larger numbers of endothelial membrane cysts (kugeln) on cerebral vessels. Vegf inhibition prevented these embryonic phenotypes, leading us to investigate specific Vegf signalling pathways. Inhibiting mTOR or MEK pathways prevented abnormal trunk and cerebral vasculature phenotypes, whereas inhibiting Nos or Mapk pathways had no effect. Combined subtherapeutic mTOR and MEK inhibition prevented vascular abnormalities, confirming synergy between these pathways in HHT. These results indicate that the HHT-like phenotype in zebrafish endoglin mutants can be mitigated through modulation of Vegf signalling. Combined low-dose MEK and mTOR pathway inhibition could represent a novel therapeutic strategy in HHT.

A digital twin is a computer-based "virtual" representation of a complex system, updated using data from the "real" twin. Digital twins are established in product manufacturing, aviation, and infrastructure and are attracting significant attention in medicine. In medicine, digital twins hold great promise to improve prevention of cardiovascular diseases and enable personalised health care through a range of Internet of Things (IoT) devices which collect patient data in real-time. However, the promise of such new technology is often met with many technical, scientific, social, and ethical challenges that need to be overcome-if these challenges are not met, the technology is therefore less likely on balance to be adopted by stakeholders. The purpose of this work is to identify the facilitators and barriers to the implementation of digital twins in cardiovascular medicine. Using, the Non-adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework, we conducted a document analysis of policy reports, industry websites, online magazines, and academic publications on digital twins in cardiovascular medicine, identifying potential facilitators and barriers to adoption. Our results show key facilitating factors for implementation: preventing cardiovascular disease, in silico simulation and experimentation, and personalised care. Key barriers to implementation included: establishing real-time data exchange, perceived specialist skills required, high demand for patient data, and ethical risks related to privacy and surveillance. Furthermore, the lack of empirical research on the attributes of digital twins by different research groups, the characteristics and behaviour of adopters, and the nature and extent of social, regulatory, economic, and political contexts in the planning and development process of these technologies is perceived as a major hindering factor to future implementation.

The use of data from smartphones and wearable devices has huge potential for population health research, given the high level of device ownership; the range of novel health-relevant data types available from consumer devices; and the frequency and duration with which data are, or could be, collected. Yet, the uptake and success of large-scale mobile health research in the last decade have not met this intensely promoted opportunity. We make the argument that digital person-generated health data are required and necessary to answer many top priority research questions, using illustrative examples taken from the James Lind Alliance Priority Setting Partnerships. We then summarize the findings from 2 UK initiatives that considered the challenges and possible solutions for what needs to be done and how such solutions can be implemented to realize the future opportunities of digital person-generated health data for clinically important population health research. Examples of important areas that must be addressed to advance the field include digital inequality and possible selection bias; easy access for researchers to the appropriate data collection tools, including how best to harmonize data items; analysis methodologies for time series data; patient and public involvement and engagement methods for optimizing recruitment, retention, and public trust; and methods for providing research participants with greater control over their data. There is also a major opportunity, provided through the linkage of digital person-generated health data to routinely collected data, to support novel population health research, bringing together clinician-reported and patient-reported measures. We recognize that well-conducted studies need a wide range of diverse challenges to be skillfully addressed in unison (eg, challenges regarding epidemiology, data science and biostatistics, psychometrics, behavioral and social science, software engineering, user interface design, information governance, data management, and patient and public involvement and engagement). Consequently, progress would be accelerated by the establishment of a new interdisciplinary community where all relevant and necessary skills are brought together to allow for excellence throughout the life cycle of a research study. This will require a partnership of diverse people, methods, and technologies. If done right, the synergy of such a partnership has the potential to transform many millions of people's lives for the better.

Cardiovascular diseases kill 18 million people each year. Currently, a patient's health is assessed only during clinical visits, which are often infrequent and provide little information on the person's health during daily life. Advances in mobile health technologies have allowed for the continuous monitoring of indicators of health and mobility during daily life by wearable and other devices. The ability to obtain such longitudinal, clinically relevant measurements could enhance the prevention, detection and treatment of cardiovascular diseases. This review discusses the advantages and disadvantages of various methods for monitoring patients with cardiovascular disease during daily life using wearable devices. We specifically discuss three distinct monitoring domains: physical activity monitoring, indoor home monitoring and physiological parameter monitoring.

Hundreds of millions of people own wearable devices capable of tracking their movement patterns. 1 Accelerometers are also increasingly the preferred tool to measure physical activity in research studies. 2 However, national and international physical activity guidelines, which recommend adults undertake at least 150–300 min of moderate intensity physical activity (MPA) or 75–150 min of vigorous intensity physical activity (VPA) per week, remain largely based on epidemiological studies in which physical activity was assessed using self-reported questionnaires. It is now known that such self-report measures generally overestimate moderate- to-vigorous physical activity (MVPA), are unlikely to accurately measure light intensity physical activity

Background: Hemodynamic wall shear stress (WSS) exerted on the endothelium by flowing blood determines the spatial distribution of atherosclerotic lesions. Disturbed flow (DF) with a low WSS magnitude and reversing direction promotes atherosclerosis by regulating endothelial cell (EC) viability and function, whereas un-DF which is unidirectional and of high WSS magnitude is atheroprotective. Here, we study the role of EVA1A (eva-1 homolog A), a lysosome and endoplasmic reticulum-associated protein linked to autophagy and apoptosis, in WSS-regulated EC dysfunction. Methods: The effect of WSS on EVA1A expression was studied using porcine and mouse aortas and cultured human ECs exposed to flow. EVA1A was silenced in vitro in human ECs and in vivo in zebrafish using siRNA (small interfering RNA) and morpholinos, respectively. Results: EVA1A was induced by proatherogenic DF at both mRNA and protein levels. EVA1A silencing resulted in decreased EC apoptosis, permeability, and expression of inflammatory markers under DF. Assessment of autophagic flux using the autolysosome inhibitor, bafilomycin coupled to the autophagy markers LC3-II (microtubule-associated protein 1 light chain 3-II) and p62, revealed that EVA1A knockdown promotes autophagy when ECs are exposed to DF, but not un-DF . Blocking autophagic flux led to increased EC apoptosis in EVA1A -knockdown cells exposed to DF, suggesting that autophagy mediates the effects of DF on EC dysfunction. Mechanistically, EVA1A expression was regulated by flow direction via TWIST1 (twist basic helix-loop-helix transcription factor 1). In vivo, knockdown of EVA1A orthologue in zebrafish resulted in reduced EC apoptosis, confirming the proapoptotic role of EVA1A in the endothelium. Conclusions: We identified EVA1A as a novel flow-sensitive gene that mediates the effects of proatherogenic DF on EC dysfunction by regulating autophagy.

Introduction Cardiovascular diseases are highly prevalent in the UK population,1 with quality of care negatively affected by accessibility and resource issues. Implementation of digital technologies into the cardiovascular care pathway has the potential to lighten the load on healthcare providers2 3; however, it is essential to embed the perspectives of clinicians.4 Aim To understand clinicians9 experiences of diagnosing heart disease and their perspectives on the use of digital health tools for diagnosis. Methods Online 1-to-1 interviews were conducted with 8 clinicians with experience in diagnosing heart disease across the UK, using a semi-structured topic guide designed to facilitate discussion surrounding their experience of making heart disease diagnoses and to gauge their perspective on the use of digital technologies within the cardiovascular care pathway. An inductive thematic analysis was applied to the data, using a coding framework to extract themes and sub-themes by the lead researcher, and subsequently validated by one independent researcher. Results A total of 8 clinician interviews were conducted. Participants were aged 35-60 years, (mean = 48.5±9.05), 62.5% identified as White British and 37.5% were female. The sample included a combination of primary and secondary care physicians, including cardiology consultants and surgeons, GPs, and a locum speciality doctor in emergency care. Within the sample, 75% had more than 20 years of experience working with heart disease patients. Data were categorised into themes including, patient communication; time and resource limitations; patient barriers to access; reliability of medical technologies and data; accessibility issues with interactive devices; and challenges with patient engagement. Conclusions The results of these interviews highlight several crucial factors to consider when developing a new technology to improve holistic diagnosis of heart disease, including interface personalisation, accessibility of technologies, and reliability of patient data and technology use. Future work building digital tools to improve the accuracy and efficiency of heart disease diagnosis can use the results of this study to create better-informed technologies with enhanced user experience and engagement. References Health Intelligence Team B. Our vision is a world free from the fear of heart and circulatory diseases. UK Factsheet. 2022. Klersy C, De Silvestri A, Gabutti G, Regoli F, Auricchio A. A Meta-Analysis of Remote Monitoring of Heart Failure Patients. J Am Coll Cardiol. 2009 Oct 27;54(18):1683‘94. Kulshreshtha A, Kvedar JC, Goyal A, Halpern EF, Watson AJ. Use of remote monitoring to improve outcomes in patients with heart failure: A pilot trial. Int J Telemed Appl [Internet]. 2010 Jan 1 [cited 2023 Feb 3];2010. Available from: https://dl.acm.org/doi/10.1155/2010/870959 Whitelaw S, Pellegrini DM, Mamas MA, Cowie M, Van Spall HGC. Barriers and facilitators of the uptake of digital health technology in cardiovascular care: a systematic scoping review. Eur Hear J - Digit Heal [Internet]. 2021 May 4 [cited 2023 Feb 3];2(1):62‘74. Conflict of Interest None

Introduction Haemodynamic wall shear stress (WSS) exerted on endothelial cells (ECs) by flowing blood determines the spatial distribution of atherosclerotic lesions. ECs are able to sense and respond to WSS thanks to mechanoreceptors on their surface, which convert mechanical forces into biochemical signals. Although several molecules and structures have been proposed to function as EC mechanoreceptors, the regulation and function of EC mechanoreceptors is poorly understood. Among EC mechanoreceptors are polycystins (PKD1 and PKD2), causative genes for autosomal dominant polycystic kidney disease, which is associated with cardiovascular abnormalities by still unknown mechanisms. Methods Zebrafish embryos were used for functional screening of EC mechanoreceptors. Candidate gene function was validated in human arterial ECs using small interfering RNA (siRNA). Atherosclerotic plaque development was assessed in mice with inducible, endothelial specific deletion of genes of interest. PKD1 downstream targets were determined using single cell RNA sequencing of ECs isolated from mouse aortas. Results A functional screening of known and putative mechanoreceptors in zebrafish revealed PKD1 and PKD2 as anti-apoptotic factors in ECs in response to haemodynamic forces. Knockdown of PKD1 or PKD2 in human arterial ECs resulted in increased EC apoptosis induced by atherogenic flow. In mice, inducible, EC-specific loss of PKD1, but not PKD2, resulted in an increase in atherosclerotic lesion development. To dissect the underlying mechanisms, we performed single cell RNA sequencing of ECs isolated from PKD1-deficient and control mice and identified a number of downstream signalling targets, which we validated in vivo and in human ECs. Conclusions By integrating in vivo and in vitro models for studying EC responses to flow with ‘omics approaches, we identified PKD1 as novel shear-sensitive regulator of EC survival and a protective factor against atherosclerosis development. This has important implications for potential therapeutic approaches for atherosclerosis. Conflict of Interest None