Skip to main content

Characterizing Atrial fibrillation by Translating its Causes into Health Modifiers in the Elderly

Periodic Reporting for period 3 - CATCH ME (Characterizing Atrial fibrillation by Translating its Causes into Health Modifiers in the Elderly)

Reporting period: 2018-05-01 to 2019-04-30

Atrial fibrillation (AF), the most common cardiac arrhythmia, is a major threat to healthy ageing, mediated by stroke, dementia, unplanned hospitalisations, heart failure, and premature death. The scientific community has identified important insights into the mechanisms that can cause AF, but the
current management of AF patients and prevention of AF are not informed by these mechanisms. As a result, we can only partially prevent AF-related morbidity and mortality, and lack effective ways to prevent AF.

The CATCH ME Consortium will bridge the present disconnect between our understanding of the mechanisms of AF and the current unstructured approach to its prevention and treatment. By combining clinical, molecular, ECG, bioinformatics, and statistical expertise, we will identify and integrate the main drivers of prevalent and incident AF in patients, and translate them into clinically useful markers. We have almost finalised a comprehensive analysis of atrial tissue and will in the near future conduct a large analysis of blood biomarkers to identify potential mechanisms of AF. We aim to identify a principal set of markers which stratify AF types and predict AF presence. Prediction models which integrate the major drivers of AF will be robustly developed and externally validated. As such, CATCH ME provides an important foundation to enable the future development of personalised strategies for management of AF in patients, e.g. laying the foundation to identify patients suitable for different AF treatments or prevention strategies.

To achieve these goals, our first aim is to describe the major alterations in atrial tissue found in patients with AF. Secondly, in parallel, we use existing patient datasets to identify drivers of AF based on alterations detectable by clinical parameters, ECG, or from peripheral blood. These alterations are associated with a wide range of measures such as biomarkers, ECG parameters, echocardiography values, etc. Thirdly, we aim to identify parameters that group patients with similar causes of AF and outcomes. We will take advantage of the databases and blood samples of 8 patient cohorts with or at risk of AF. Outcomes from these atrial tissue, clinical, and blood biomarker analyses will be combined and a prognostic modelling approach will yield clinical prediction models with markers which best predict AF incidence, prevalence, and adverse outcomes. The model will be externally validated with a view to incident and recurrent AF as well as to AF-related complications.

Finally, we aim to transform the prevention and treatment of AF by providing IT tools based on evidence-based AF management at the beginning of the project, and by providing tools to discern patients with different types of AF to health care professionals and the general public in later stages of the project. To achieve this, we will develop two mobile applications targeted at healthcare professionals and patients, develop a training programme to implement our project outcomes in routine clinical care, and carry out a health economic review, alongside presenting results at academic conferences, publishing papers in high impact journals and continually communicating about the project via social media and our website.
After four years, the project has been completed successfully and the major outcomes will create a platform for continued work by the members of the consortium.

The project provided one of the largest atrial tissue collections analysed so far.

An automated analysis of the hallmarks of atrial structural remodelling was developed. Validation to manual annotation showed that detection of cardiomyocytes occurred with precision (95,4 ± 4,5%), while maintaining sensitivity (93,9 ± 5.5%). A comparison between samples from patient with and without atrial fibrillation and/or heart failure indicate that endomysial fibrosis is driven by rhythm status, whereas increasing cardiomyocyte diameters occur predominantly in heart failure patients.

It was found that the atrial epicardium is a source of fibroblasts that can invade the neighboring myocardium. These epicardial-derived fibroblasts arise from the epithelial-to-mesenchymal transition and differentiation of a subset of resident epicardial progenitors, a process regulated by distinct signaling pathways and peptides. These results have already started impacting AF risk assessment, preventive strategies, and therapeutic stratifications through successful dissemination efforts at meetings and through publications relating to circulating microRNA, telomere length analysis, ECG based analyses with respect to AF complexity and P wave signals, assessment of atrial fibrosis, and endurance training. These dissemination efforts impacted the wider awareness for AF and set the stage for the planned novel AF classification evolving from CATCH ME.
Models including only clinical variables have been developed to predict recurrent and prevalent AF. These models demonstrated to be accurate and to overperform other commonly used scores such as CHADS, APPLE, HATCH and ATLAS.

Seven hypotheses had been designed a priori focusing on the association between some health modifiers and therapies, and AF prevalence or recurrence risk. These hypotheses have been tested in the CATCHME cohort.

A database has been created with a contribution from the majority of the consortium will be used for further analyses by the partners. The database will also be made available for further analyses to other researchers upon request.

Two apps have been developed for both Apple and android devices. These can be downloaded by searching for My AF (patients) or AF Manager (clinicians). These are being successfully used and the patient app has been translated into German, Dutch, French, Spanish Italian, Polish and Portuguese.
More than 30 new biomarker opportunities have been identified, where investigations and validations are still ongoing. One of the first important outcomes showed that the fibrotic pathway (FGF23) plays an important role in AFib. The markers showed good performance to predict recurrence of AFib and also diagnostic use. In analogy, the marker NT-proBNP was confirmed. This work will carry on post project.

We have contributed to academic as well as non-academic events, engaged via social media, press releases and websites. Fifty papers have been published in high impact journals and the consortium expect this number to increase. Details can be found on our web site www.catch-me.info


A short summary of the CATCH ME outcomes has been put into a booklet. This booklet has been made publicly available on the CATCH ME website http://www.catch-me.info/sites/default/files/docs/CATCH%20ME_Key%20work_final.pdf
The project provided one of the largest atrial tissue collections analysed so far.

Fifty papers have been published in high impact journals and the consortium expect this number to increase.

The project developed a novel system that efficiently merges and integrates clinical databases from very different data sources. Our combined database will provide in-depth information on one of the largest, most varied, and comprehensive AF population, enabling prognostic analysis at a scale and depth, which was not possible before.

More than 30 new biomarker opportunities have been identified, where investigations and validations are still ongoing. This work will carry on post project.
Proposed roadmap for developing a classification of AF based on the major health modifiers.