Why euCanSHare?

Despite continuous advances in diagnosis and treatment, cardiovascular diseases (CVDs) remain the main cause of death worldwide. CVDs account for about 17.9 million annual deaths1 – 31% of all deaths worldwide – and greatly reduce the quality of life of affected patients, challenging the sustainability of modern healthcare systems. In Europe, CVDs are responsible for 30.4% and 25.3% of deaths before the age of 65, in men and women, respectively.2

Many cardiovascular drugs have shown limited efficacy on general populations. Personalised medicine approaches offer solutions to improve risk assessment, early diagnosis and patient-tailored treatment protocols. Data-driven, multi-cohort approaches are needed to link molecular, imaging, functional and clinical data. However, such integration presents a formidable challenge in terms of data storage and access frameworks, interoperability and IT architectures, especially across diverse jurisdictions.

1 World Health Organisation (WHO), “Fact sheet on CVDs” (May 2017). 2 Timmis A, Townsend N, Gale C, Grobbee R, Maniadakis N, Flather M, Wilkins E, Wright L, Vos R, Bax J, Blum M, Pinto F, Vardas P.
European Society of Cardiology. Cardiovascular Disease Statistics 2017. Eur Heart J 2017; 39(7):508-579.

Our mission

euCanSHare is a joint EU-Canada project to establish a cross-border data sharing and multi-cohort cardiovascular research platform. Specifically, the project will integrate data infrastructures, IT solutions and data sources from EU, Canada and other countries into a web-based data access system with functionalities for increased efficiency in cardiovascular data-driven research. euCanSHare integrates more than 35 Canadian and European cohorts making up over 1 million records and actively seeks to expand to other regions.

euCanSHare key objectives are


implement a FAIR (Findable, Accessible, Interoperable and Re-usable) data platform for enhanced data sharing and big data research in cardiology


leverage the power of multi-cohort and multi-omics data in personalised cardiovascular medicine, addressing image analysis and bioinformatics, biomarker identification and quantification, knowledge discovery and risk assessment


apply legal frameworks to enable compliant data sharing across countries in line with Open Science tenets


provide the euCanSHare platform with capabilities to extend over time and thus create the largest network of cohorts and researchers in cardiovascular personalised medicine for academia, companies and public authorities


Phase I
(1 Dec 2018 – 30 Nov 2019)
  • Platform design, definition of user requirements and technical specifications
  • Delineation of the legal and ethical framework to allow data sharing across EU and Canada
  • Preliminary users engagement: definition of incentives to attract participants
Development of the first prototype of the euCanSHare platform for internal testing
Phase II
(1 Dec 2019 – 30 Nov 2020)
  • Implementation of platform interfaces and services, including user-designed functionalities, data harmonisation and analysis
  • Initial user tests and feedback gathering
First release of the euCanSHare platform within the consortium
Phase III
(1 Dec 2020 – 30 Nov 2022)
  • Four pilot-test research studies, feedback gathering and system refinements:
    • Two research studies on multi-domain risk predictors in cardiovascular disease, integrating imaging, genetics, lifestyle and sex
    • One public health study to compare risk estimates across countries and regions
    • One industry-relevant study on drug target discovery to adjust commercial platform requirements
  • Workshops with hands-on sessions to foster the expansion of the platform with new additional cohorts and users
Final deployment of the platform for the entire cardiovascular research community

Ethical and legal commitments

The partners and participating cohorts in the euCanSHare consortium will comply with the International, European and Canadian Legal Framework and relevant ethical standards and guidelines. In particular, the consortium will comply with relevant national and EU legislation and guidelines, including:

  • The Declaration of Helsinki in its latest version;
  • The charter of fundamental rights of the EU (2000/C 364/01);
  • The principles enshrined in the Oviedo Bioethics Convention;
  • OECD guidelines and policy on privacy and data sharing
  • Recommendation CM/Rec (2016)6 of the Committee of Ministers to Member States on research on biological materials of human origin.
  • EU regulation 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data (GDPR);
  • Canadian Personal Information Protection and Electronic Documents Act, S.C. 2000 c. 5 (PIPEDA);
  • Canadian Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans, Canadian Institutes of Health Research, Natural Sciences and Engineering Research Council of Canada, and Social Sciences and Humanities Research Council of Canada, December 2014.

Furthermore, the consortium will ensure continuing compliance and will take into account relevant revisions to the mentioned legislation and directives. Each partner and participating cohort will be held responsible for fulfillment of all legal and ethical requirements in its country. In addition, the partners and participating cohorts in the euCanSHare consortium will uphold the following principles to incentivize and promote data sharing and ensure the secure management of data:

  • Data minimisation principle
  • Informed Consent
  • Guaranteeing patient’s confidentiality
  • Secure handling of data
  • Data anonymisation
  • Transparency

The partners and participating cohorts in the euCanSHare consortium are aware of their duties and are supported by the team of WP1 (Socio-ethical and legal interoperability analysis) and the external ethical & legal advisor, Maria Pilar Nicholas, to ensure that all ethical and legal requirements are constantly met.

Publications and materials

Peer-review publications

Börschel, C.S., Geelhoed, B., Niiranen, T., Camen, S., Donati, M.B., Havulinna, A.S., Gianfagna, F., Palosaari, T., Jousilahti, P., Kontto, J., Vartiainen, E., Ojeda, F.M., den Ruijter, H.M., Costanzo, S., de Gaetano, G., Di Castelnuovo, A., Linneberg, A., Vishram-Nielsen, J.K., Løchen, M.L., Koenig, W., Jørgensen, T., Kuulasmaa, K., Blankenberg, S., Iacoviello, L., Zeller, T., Söderberg. S., Salomaa, V., Schnabel, R.B. (2023). “Risk prediction of atrial fibrillation and its complications in the community using hs troponin I.” European Journal of Clinical Investigation

Atrial fibrillation (AF) is becoming increasingly common. Traditional cardiovascular risk factors (CVRF) do not explain all AF cases. Blood-based biomarkers reflecting cardiac injury such as high-sensitivity troponin I (hsTnI) may help close this gap. The study investigated the predictive ability of hsTnI for incident AF in 45,298 participants (median age 51.4 years, 45.0% men) across European community cohorts in comparison to CVRF and established biomarkers (C-reactive protein, N-terminal pro B-type natriuretic peptide). hsTnI as a biomarker of myocardial injury does not improve prediction of AF incidence beyond classical CVRF and NT-proBNP. However, it is associated with the AF-related disease heart failure and mortality likely reflecting underlying subclinical cardiovascular impairment.

Toprak, B., Brandt, S., Brederecke, J., Gianfagna, F., Vishram-Nielsen, J.K.K., Ojeda, F.M., Costanzo, S., Börschel, C.S., Söderberg, S., Katsoularis, I., Camen, S., Vartiainen, E., Donati, M.B., Kontto, J., Bobak, M., Mathiesen, E.B., Linneberg, A., Koenig, W., Løchen, M.L., Di Castelnuovo, A., Blankenberg, S., de Gaetano, G., Kuulasmaa, K., Salomaa, V., Iacoviello, L., Niiranen, T., Zeller, T., Schnabel, R.B. (2023). “Exploring the incremental utility of circulating biomarkers for robust risk prediction of incident atrial fibrillation in European cohorts using regressions and modern machine learning methods.” Europace

To identify robust circulating predictors for incident atrial fibrillation (AF) using classical regressions and machine learning (ML) techniques within a broad spectrum of candidate variables. In pooled European community cohorts (n = 42 280 individuals), 14 routinely available biomarkers mirroring distinct pathophysiological pathways including lipids, inflammation, renal, and myocardium-specific markers (N-terminal pro B-type natriuretic peptide [NT-proBNP], high-sensitivity troponin I [hsTnI]) were examined. Of 42 280 individuals (21 843 women [51.7%]; median [interquartile range, IQR] age, 52.2 [42.7, 62.0] years), 1496 (3.5%) developed AF during a median follow-up time of 5.7 years. Using different variable selection procedures including ML methods, NT-proBNP consistently remained the strongest blood-based predictor of incident AF and ranked before classical cardiovascular risk factors.

Sujana, C., Salomaa, V., Kee, F., Seissler, J., Jousilahti, P., Neville, C., Then, C., Koenig, W., Kuulasmaa, K., Reinikainen, J., Blankenberg, S., Zeller, T., Herder, C., Mansmann, U., Peters, A., Thorand, B. (2022). “BiomarCaRE Consortium. Associations of the vasoactive peptides CT-proET-1 and MR-proADM with incident type 2 diabetes: results from the BiomarCaRE Consortium.” Cardiovascular Diabetology

Endothelin-1 (ET-1) and adrenomedullin (ADM) are commonly known as vasoactive peptides that regulate vascular homeostasis. Less recognised is the fact that both peptides could affect glucose metabolism. This study investigated whether ET-1 and ADM, measured as C-terminal-proET-1 (CT-proET-1) and mid-regional-proADM (MR-proADM), respectively, were associated with incident type 2 diabetes. Higher concentrations of CT-proET-1 and MR-proADM are associated with incident type 2 diabetes, but our Mendelian randomisation analysis suggests a probable causal link for CT-proET-1 only. The association of MR-proADM seems to be modified by body composition.

Camen, S., Csengeri, D., Geelhoed, B., Niiranen, T., Gianfagna, F., Vishram-Nielsen, J.K., Costanzo, S., Söderberg, S., Vartiainen, E., Börschel, C.S., Donati, M.B., Løchen, M.L., Ojeda, F.M., Kontto, J., Mathiesen, E.B., Jensen, S., Koenig, W., Kee, F., de Gaetano, G., Zeller, T., Jørgensen, T., Tunstall-Pedoe, H., Blankenberg, S., Kuulasmaa, K., Linneberg, A., Salomaa, V., Iacoviello, L., Schnabel, R.B. (2022). “Risk factors, subsequent disease onset, and prognostic impact of myocardial infarction and atrial fibrillation.” Journal of the American Heart Association

Although myocardial infarction (MI) and atrial fibrillation (AF) are frequent comorbidities and share common cardiovascular risk factors, the direction and strength of the association of the risk factors with disease onset, subsequent disease incidence, and mortality are not completely understood. Temporal relations of disease onset and identified predictors of MI, AF, and all‐cause mortality in 108 363 individuals (median age, 46.0 years; 48.2% men) free of MI and AF at baseline from 6 European population‐based cohorts have been examined. Different associations of cardiovascular risk factors with both diseases indicating distinct pathophysiological pathways have been observed. Subsequent diagnoses of MI and AF significantly increased mortality risk.

Rosberg, V., Vishram-Nielsen, J.K., Kristensen, A.M.D., Pareek, M., Sehested, T.S.G., Nilsson, P.M., Linneberg, A., Palmieri, L., Giampaoli, S., Donfrancesco, C., Kee, F., Mancia, G., Cesana, G., Veronesi, G., Grassi, G., Kuulasmaa, K., Salomaa, V., Palosaari, T., Sans, S., Ferrieres, J., Dallongeville, J., Söderberg, S., Moitry, M., Drygas, W., Tamosiunas, A., Peters, A., Brenner, H., Schöttker, B., Grimsgaard, S., Biering-Sørensen, T., Olsen, M.H. (2022). “Simple cardiovascular risk stratification by replacing total serum cholesterol with anthropometric measures: The MORGAM prospective cohort project.” Preventive Medicine Reports

To assess whether anthropometric measures (body mass index [BMI], waist-hip ratio [WHR], and estimated fat mass [EFM]) are independently associated with major adverse cardiovascular events (MACE), and to assess their added prognostic value compared with serum total-cholesterol. The study population comprised 109,509 individuals (53% men) from the MORGAM-Project, aged 19–97 years, without established cardiovascular disease, and not on antihypertensive treatment. While BMI was reported in all, WHR and EFM were reported in about 52,000 participants. The study found a potential role for replacing total-cholesterol with anthropometric measures for MACE-prediction among individuals < 50 years when laboratory measurements are unavailable, but not among those ≥ 50 years.

Oskarsson, V., Eliasson, M., Salomaa, V., Reinikainen, J., Männistö, S., Palmieri, L., Donfrancesco, C., Sans, S., Costanzo, S., de Gaetano, G., Iacoviello, L., Veronesi, G., Ferrario, M.M., Padro, T., Thorand, B., Huth, C., Zeller, T., Blankenberg, S., Anderson, A.S., Tunstall-Pedoe, H., Kuulasmaa, K., Söderberg, S. (2021). “BiomarCaRE investigators. Influence of geographical latitude on vitamin D status: cross-sectional results from the BiomarCaRE consortium.” British Journal of Nutritrion

Even though sunlight is viewed as the most important determinant of 25-hydroxyvitamin D (25(OH)D) status, several European studies have observed higher 25(OH)D concentrations among north-Europeans than south-Europeans. The association between geographical latitude (derived from ecological data) and 25(OH)D status in six European countries has been studied, using harmonised immunoassay data from 81 084 participants in the Biomarkers for Cardiovascular Risk Assessment in Europe (BiomarCaRE) project (male sex 48·9 %; median age 50·8 years; examination period 1984–2014). In conclusion, the previous observation of a north-to-south gradient of 25(OH)D status in Europe has been confirmed, with higher percentile values among north-Europeans than south-Europeans.

Rehm, M., Rothenbacher, D., Iacoviello, L., Costanzo, S., Tunstall-Pedoe, H., Fitton, C.A., Söderberg, S., Hultdin, J., Salomaa, V., Jousilahti, P., Palosaari, T., Kuulasmaa, K., Waldeyer, C., Schnabel, R.B., Zeller, T., Blankenberg, S., Koenig, W. (2021). “BiomarCaRE Consortium. Chronic kidney disease and risk of atrial fibrillation and heart failure in general population-based cohorts: the BiomarCaRE project.” ESC Heart Failure

Chronic kidney disease (CKD) has a complicated relationship with the heart, leading to many adverse outcomes. The aim of this study was to evaluate the relationship between CKD and the incidence of atrial fibrillation (AF) and heart failure (HF) along with mortality as a competing risk in general population cohorts. This study was conducted within the BiomarCaRE project using harmonized data from 12 European population-based cohorts (n = 48 518 participants). Renal function was assessed by glomerular filtration rate estimated using the combined Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation with standardized serum creatinine (Cr) and non-standardized serum cystatin C (CysC). Incidence of AF and HF respectively, during a median follow-up of 8 years was recorded. The HR for AF in patients with CKD compared with patients without CKD was 1.28 (95% confidence interval 1.07–1.54) after adjustment for covariates. Chronic kidney disease is an independent risk factor for subsequent AF and is even more closely associated with HF. For HF, in addition, elevated levels of hs-C-reactive protein at baseline were related to incident events.

Sinning, C., Makarova, N., Völzke, H., Schnabel, R.B., Ojeda, F., Dörr, M., Felix, S.B., Koenig, W., Peters, A., Rathmann, W., Schöttker, B., Brenner, H., Veronesi, G., Cesana, G., Brambilla, P., Palosaari, T., Kuulasmaa, K., Njølstad, I., Mathiesen, E.B., Wilsgaard, T., Blankenberg, S., Söderberg, S., Ferrario, M.M., Thorand, B. (2021). “Association of glycated hemoglobin A 1c levels with cardiovascular outcomes in the general population: results from the BiomarCaRE (Biomarker for Cardiovascular Risk Assessment in Europe) consortium.” Cardiovasc Diabetology

This study aims to assess the association of Glycated hemoglobin A1c (HbA1c) with cardiovascular outcomes in the general population. Biomarkers may contribute to improved cardiovascular risk estimation. HbA1c is used to monitor the quality of diabetes treatment. Its strength of association with cardiovascular outcomes in the general population remains uncertain. Data from six prospective population-based cohort studies across Europe comprising 36,180 participants were analyzed. HbA1c was evaluated in conjunction with classical cardiovascular risk factors (CVRFs) for association with cardiovascular mortality, cardiovascular disease (CVD) incidence, and overall mortality in subjects without diabetes (N = 32,496) and with diabetes (N = 3684). HbA1c is independently associated with cardiovascular mortality, overall mortality, and cardiovascular disease in the general European population.

Vuori, M.A., Reinikainen, J., Söderberg, S., Bergdahl, E., Jousilahti, P., Tunstall-Pedoe, H., Zeller, T., Westermann, D., Sans, S., Linneberg, A., Iacoviello, L., Costanzo, S., Salomaa, V., Blankenberg, S., Kuulasmaa, K., Niiranen, T.J. (2021). “Diabetes status-related differences in risk factors and mediators of heart failure in the general population: results from the MORGAM/BiomarCaRE consortium.” Cardiovasc Diabetology

The risk of heart failure among diabetic individuals is high, even under tight glycemic control. The correlates and mediators of heart failure risk in individuals with diabetes need more elucidation in large population-based cohorts with long follow-up times and a wide panel of biologically relevant biomarkers. In a population-based sample of 3834 diabetic and 90,177 non-diabetic individuals, proportional hazards models and mediation analysis were used to assess the relation of conventional heart failure risk factors and biomarkers with incident heart failure. The findings suggest that the main mediators of heart failure in diabetes are obesity, hyperglycemia, and cardiac strain/volume overload. Conventional cardiovascular risk factors are strongly related to incident heart failure, but these associations are not stronger in diabetic than in non-diabetic individuals.

Sujana, C., Salomaa, V., Kee, F., Costanzo, S., Söderberg, S., Jordan, J., Jousilahti, P., Neville, C., Iacoviello, L., Oskarsson, V., Westermann, D., Koenig, W., Kuulasmaa, K., Reinikainen, J., Blankenberg, S., Zeller, T., Herder, C., Mansmann, U., Peters, A., Thorand, B.; BiomarCaRE Consortium. (2021). “Natriuretic Peptides and Risk of Type 2 Diabetes: Results From the Biomarkers for Cardiovascular Risk Assessment in Europe (BiomarCaRE) Consortium.” Diabetes Care

Natriuretic peptide (NP) concentrations are increased in cardiovascular diseases (CVDs) but are associated with a lower diabetes risk. Associations of N-terminal pro-B-type NP (NT-proBNP) and midregional proatrial NP (MR-proANP) with incident type 2 diabetes stratified by the presence of CVD have been investigated. The study found that NT-proBNP and MR-proANP are inversely associated with incident type 2 diabetes. However, the inverse association of NT-proBNP seems to be modified by the presence of CVD.

Morseth, B., Geelhoed, B., Linneberg, A., Johansson, L., Kuulasmaa, K., Salomaa, V., Iacoviello, L., Costanzo. S., Söderberg, S., Niiranen, T.J., Vishram-Nielsen, J.K.K., Njølstad, I., Wilsgaard, T., Mathiesen, E.B., Løchen, M.L., Zeller, T., Blankenberg, S., Ojeda, F.M., Schnabel, R.B.; MORGAM consortium. (2021). “Age-specific atrial fibrillation incidence, attributable risk factors and risk of stroke and mortality: results from the MORGAM Consortium.” OpenHeart

The aim of this study was to examine age-specific risk factor associations with incident atrial fibrillation (AF) and their attributable fraction in a large European cohort. With individual-level data (n=66 951, 49.1% men, age range 40–98 years at baseline) from five European cohorts of the MOnica Risk, Genetics, Archiving and Monograph Consortium, the participants were followed for incident AF for up to 10 years and the association with modifiable risk factors from the baseline examinations (body mass index (BMI), hypertension, diabetes, daily smoking, alcohol consumption and history of stroke and myocardial infarction (MI)). Additionally, the participants were followed up for incident stroke and all-cause mortality after new-onset AF. In this large European cohort aged 40 years and above, risk of AF was largely attributed to BMI, high alcohol consumption and a history MI or stroke from middle age. Thus, preventive measures for AF should target risk factors such as obesity and hypertension from early age and continue throughout life.

SCORE2 working group and ESC Cardiovascular risk collaboration. (2021). “SCORE2 risk prediction algorithms: new models to estimate 10-year risk of cardiovascular disease in Europe.” European Heart Journal

This study aimed to develop, validate, and illustrate an updated prediction model (SCORE2) to estimate 10-year fatal and non-fatal cardiovascular disease (CVD) risk in individuals without previous CVD or diabetes aged 40–69 years in Europe. Risk prediction models have been derived using individual-participant data from 45 cohorts in 13 countries (677 684 individuals, 30 121 CVD events). The new algorithm, SCORE2, enhances the identification of individuals at higher risk of developing CVD across Europe.

Devriendt, T., Shabani, M., Lekadir, K., Borry, P. (2022). “Data sharing platforms: instruments to inform and shape science policy on data sharing?” Scientometrics

To enhance data sharing, platforms are being constructed to make clinical cohort data more findable, accessible, interoperable, and reusable. However, they can only facilitate data sharing through technical means, and may not be able of fully resolving the data sharing problem. In this article, it is shown how their design may help in addressing policy barriers to data sharing in the long-term.

Devriendt, T. with Devriendt, T. (corresp. author) (2022). “Credit and Recognition for Contributions to Data-Sharing Platforms Among Cohort Holders and Platform Developers in Europe: Interview Study.” Journal Of Medical Internet Research

The European Commission is funding projects that aim to establish data-sharing platforms to enhance and facilitate the international sharing of cohort data. However, broad data sharing may be restricted by the lack of adequate recognition for those who share data. The aim of this study is to describe in depth the concerns about acquiring credit for data sharing within epidemiological research.

Devriendt, T., Borry, P., Shabani, M. with Devriendt, T. (corresp. author) (2021). “Factors that influence data sharing through data sharing platforms: A qualitative study on the views and experiences of cohort holders and platform developers.” PLOS ONE

Empirical studies show that many barriers impede sharing data broadly, although Infrastructures are being developed to enhance and facilitate the sharing of cohort data internationally. Seventeen participants involved in developing data sharing platforms or tied to cohorts that are to be submitted to platforms were recruited for semi-structured interviews to share views and experiences regarding data sharing. Data generators might be unwilling to contribute and share for non-collaborative work if no financial resources are provided for sharing data.

Devriendt, T., Shabani, M., Borry, P. (2022). “Policies to regulate data sharing of cohorts via data infrastructures: An interview study with funding agencies.” International Journal of Medical Informatics

To encourage data sharing, members of funding agencies were recruited to participate in semi-structured interviews, analyzed through inductive content analysis, to understand their perspectives on policy interventions. Policy measures restricting the decision-making authority of researchers in terms of data sharing are not generally supported. Thus, concrete steps are proposed to enable evidence-based policy making.

Cetin, I., Stephens, M., Camara, O., González Ballester, M. A., (2023), “Attribute-based interpretable representations of medical images with variational autoencoders”; Computerized Medical Imaging and Graphics

Deep learning (DL) methods are required to better understand the relationship of clinical and imaging-based attributes with DL outcomes. Thus, facilitating their use in the reasoning behind the medical decisions. In this paper, a variational autoencoders (VAE) approach is proposed, the Attri-VAE, that includes an attribute regularization term to associate clinical and medical imaging attributes with different regularized dimensions in the generated latent space, enabling a better interpretation of the attributes.

Pujadas,E. R., Raisi-Estabragh, Z., Szabo, L., McCracken, C., Izquierdo Morcillo, C., Campello, V. M., Martín-Isla, C., M. Atehortua, A. M., Vago, H., Merkely, B., Maurovich-Horvat, P., Harvey, N. C., Neubauer, S., Petersen, S. E., Lekadir, K., (2022), “Prediction of incident cardiovascular events using machine learning and CMR radiomics”; European Radiology

Atrial fibrillation (AF) prediction model using jointly the vascular risk factor (VRF), the cardiovascular magnetic resonance (CMR), and the Rad model obtained the best result, while heart failure (HF) showed the most significant improvement with the inclusion of CMR metrics. Moreover, adding only the radiomics features to the VRF reached an almost similarly good performance. Prediction models looking into incident myocardial infarction (MI) and stroke reached slightly smaller improvement. In conclusion, the experiments show that radiomics features can provide incremental predictive value over VRF and CMR indices in the prediction of incident cardiovascular diseases (CVDs).

Ruiz Pujadas, E., Raisi-Estabragh, Z., Szabo, L., Morcillo, C. I., Campello, V. M., Martin-Isla, C., Vago, H., Merkely, B., Harvey, N. C., Petersen, S. E., Lekadir, K., (2022), “Atrial fibrillation prediction by combining ECG markers and CMR radiomics”, Scientific Reports

Atrial fibrillation (AF) is the most common cardiac arrhythmia. It is associated with a higher risk of important adverse health outcomes such as stroke and death. AF is linked to distinct electro-anatomic alterations. The main tool for AF diagnosis is the Electrocardiogram (ECG). However, an ECG recorded at a single time point may not detect individuals with paroxysmal AF. In this study, machine learning models for discrimination of prevalent AF have been developed by using a combination of image-derived radiomics phenotypes and ECG features. The integrative model including radiomics and ECG together resulted in a better performance than ECG alone, particularly in women, and presents a potential novel approach for earlier detection of AF.

Zhuang, X., Xu, J., Luo, X., Chen, C., Ouyang, C., Rueckert, D., Campello, V. M., Lekadir, K., Vesal, S., Kumar, N.R., Liu, Y., Luo, G., Chen, J., Li, H., Ly, B., Sermesant, M., Roth, H., Zhu, W., Li, L., (2022), "Cardiac segmentation on late gadolinium enhancement MRI: A benchmark study from multi-sequence cardiac MR segmentation challenge", Science Direct, Medical Image Analysis, Elsevier

This paper presents the selective results from the Multi-Sequence Cardiac MR (MS-CMR) Segmentation challenge, offering a data set of paired MS-CMR images, including auxiliary CMR sequences as well as Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR), from 45 patients who underwent cardiomyopathy.

Mariño, J., Kasbohm, E., Struckmann, S., Kapsner, L.A., Schmidt, C.O., (2022), “R Packages for Data Quality Assessments and Data Monitoring: A Software Scoping Review with Recommendations for Future Developments”, Applied Sciences, MDPI

This paper reviews R packages related to data quality and assesses their scope assessed against a DQ framework for observational health studies. The study found that the packages’ scope varies considerably regarding functionalities and usability. Only three packages follow a DQ concept, and some offer an extensive rule-based issue analysis.

Devriendt, T., Shabani, M., Lekadir, K., Borry, P., (2022), “Data sharing platforms: instruments to inform and shape science policy on data sharing?”, Scientometrics, Springer

This paper describes how platforms can be used to inform science policy development, to monitor data sharing practices and to steer funding prioritization for cohorts and data infrastructures.

Raisi-Estabragh, Z., McCracken, C., Condurache, D., Aung, N., Vargas, J.D., Hafiz Naderi, Munroe P.B., Neubauer S., Harvey, N.C., Petersen, S. E. (2021). “Left atrial structure and function are associated with cardiovascular outcomes independent of left ventricular measures: a UK Biobank CMR study”. European Heart Journal - Cardiovascular Imaging

This paper evaluated the associations of left atrial structure and function with prevalent and incident cardiovascular disease, independent of left ventricular metrics, in 25 896 UK Biobank participants.

Raisi-Estabragh, Z., Jaggi, A., Gkontra, P., McCracken, C., Aung, N., Munroe, P.B., Neubauer, S., Harvey, N.C., Lekadir, K., Petersen, S.E. (2021). “Cardiac Magnetic Resonance Radiomics Reveal Differential Impact of Sex, Age, and Vascular Risk Factors on Cardiac Structure and Myocardial Tissue”. Frontiers In Cardiovascular Medicine

This paper studied variation in cardiovascular magnetic resonance (CMR) radiomics phenotypes by age and sex in healthy UK Biobank participants.

Abdulkareem, M., Brahier, M.S., Zou, F., Taylor, A., Thomaides, A., Bergquist, P.J., Srichai, M.B., Lee, A.M., Vargas, J.D., Petersen, S.E. (2022). “Generalizable Framework for Atrial Volume Estimation for Cardiac CT Images Using Deep Learning With Quality Control”. Frontiers In Cardiovascular Medicine

This study proposed a framework of several processes that perform a combination of tasks using a dataset of Cardiac computed tomography (CCT) images from patients.

Izquierdo, C., Guillem Casas, G. Martin-Isla, C., Campello, V.M., Guala, A., Polyxeni Gkontra, P., Rodríguez-Palomares, J.F. , K. (2021). "Radiomics-Based Classification of Left Ventricular Non-compaction, Hypertrophic Cardiomyopathy, and Dilated Cardiomyopathy in Cardiovascular Magnetic Resonance". Frontiers In Cardiovascular Medicine

This paper proposes a radiomics approach to automatically encode differences in the underlying shape, gray-scale and textural information in the myocardium and its trabeculae, which may enhance the capacity to differentiate between these overlapping conditions.

Rauseo, E., Izquierdo Morcillo, C., Raisi-Estabragh, Z., Polyxeni Gkontra, P., Aung, N., Lekadir,, K., E. Petersen, S. E. (2021). “New Imaging Signatures of Cardiac Alterations in Ischaemic Heart Disease and Cerebrovascular Disease Using CMR Radiomics.” Frontiers in Cardiovascular Medicine

This study demonstrates the potential value of CMR radiomics over conventional indices in detecting subtle cardiac changes associated with chronic ischaemic processes involving the brain and heart, even in the presence of more heterogeneous clinical pictures. Radiomics analysis might also improve our understanding of the complex mechanisms behind the brain-heart interactions during ischaemia.

Bernier, & A. Knoppers, B. M. (2021). "Longitudinal Health Studies: Secondary Uses Serving the Future". Biopreservation and Biobanking.

The research compares the ethical and institutional conditions that govern the sharing and secondary use of longitudinal population health data from multiple cohorts. The results demonstrate that data of longitudinal population health cohorts assessed can generally be shared and used for secondary purposes.

Devriendt, T., Borry, P., & Shabani, M. (2021). "Factors that influence data sharing through data sharing platforms: A qualitative study on the views and experiences of cohort holders and platform developers". Plos one, 16(7), e0254202.

The paper describes the barriers and concerns for the sharing of cohort data, including credit and recognition, the potential misuse of data, loss of control, lack of resources, socio-cultural factors and ethical and legal barriers, and the implications for data sharing platforms.

Hageman, S., Pennells, L., Ojeda, F., Kaptoge, S., Kuulasmaa, K., de Vries, T., ... & Völzke, H. "SCORE2 risk prediction algorithms: new models to estimate 10-year risk of cardiovascular disease in Europe." European Heart Journal.

This study developed, validated and illustrated an updated prediction model (SCORE2) to estimate 10-year fatal and non-fatal cardiovascular disease (CVD) risk in individuals without previous CVD or diabetes aged 40–69 years in Europe.

Devriendt, T., Shabani, M., & Borry, P. (2021). "Data Sharing in Biomedical Sciences: A Systematic Review of Incentives." Biopreservation and Biobanking.

This article provides a systematic review of the current and proposed incentive mechanisms for researchers in biomedical sciences and discusses their strengths and weaknesses.

Schmidt, C. O., Struckmann, S., Enzenbach, C., Reineke, A., Stausberg, J., Damerow, S., ... & Richter, A. (2021). "Facilitating harmonized data quality assessments. A data quality framework for observational health research data collections with software implementations in R." BMC Medical Research Methodology, 21(1), 1-15.

This work introduces a data quality framework for observational health research data collections with supporting software implementations to facilitate harmonized data quality assessments.

Richter, A., Schmidt, C. O., Krüger, M., & Struckmann, S. (2021). "dataquieR: assessment of data quality in epidemiological research." Journal of Open Source Software, 6(61), 3093.

dataquieR is an R package to conduct data quality assessments in data collections designed for research, making strong use of metadata that specify the requirements of the study data. Particular focus is placed on the influence of observers, examiners, and devices on the measurement process.

Raisi-Estabragh, Z., Gkontra, P., Jaggi, A., Cooper, J., Augusto, J., Bhuva, A. N., ... & Petersen, S. E. (2020). "Repeatability of Cardiac Magnetic Resonance Radiomics: A Multi-Centre Multi-Vendor Test-Retest Study." Frontiers in cardiovascular medicine, 7, 289.

The study evaluates the repeatability of cardiac magnetic resonance (CMR) radiomics features on test-retest scanning using a multi-centre multi-vendor dataset with a varied case-mix

Cetin, I., Raisi-Estabragh, Z., Petersen, S. E., Napel, S., Piechnik, S. K., Neubauer, S., ... & Lekadir, K. (2020). "Radiomics signatures of cardiovascular risk factors in cardiac MRI: Results from the UK Biobank." Frontiers in cardiovascular medicine, 7.

The paper addresses the performance of Cardiovascular magnetic resonance (CMR) radiomics models for identifying changes in cardiac structure and tissue texture due to cardiovascular risk factors.

Raisi-Estabragh, Z., Harvey, N. C., Neubauer, S., & Petersen, S. E. (2020). "Cardiovascular magnetic resonance imaging in the UK Biobank: a major international health research resource." European Heart Journal-Cardiovascular Imaging.

The article considers how we may best utilize the UKB CMR data to advance cardiovascular research and review notable achievements to date

Cetin, I., Raisi-Estabragh, Z., Petersen, S. E., Napel, S., Piechnik, S. K., Neubauer, S., ... & Lekadir, K. (2020). "Radiomics signatures of cardiovascular risk factors in cardiac MRI: Results from the UK Biobank." Frontiers in Cardiovascular Medicine, 7.

The article analyses and confirms the feasibility and potential of CMR radiomics for deeper image phenotyping of cardiovascular health and disease

De Sutter, E., Zaçe, D., Boccia, S., Di Pietro, M. L., Geerts, D., Borris, P., & Huys, I. (2020). "Implementation of Electronic Informed Consent in Biomedical Research and Stakeholders' Perspectives: Systematic Review." Journal of medical Internet research, 22(10), p.e19129.

The review provides an overview of the ethical, legal, regulatory, and user interface perspectives of multiple stakeholder groups in order to assist responsible implementation of electronic informed consent in biomedical research.

Bovenberg, J., Peloquin, D., Bierer, B., Barnes, M., & Knoppers, B. M. (2020). How to fix the GDPR's frustration of global biomedical research. Science, 370(6512), 40-42.

The article examines whether there is room under the GDPR for EU biomedical researchers to share data from the EU with the rest of the world to facilitate biomedical research, and proposes solutions for consideration by either the EU legislature, the EU Commission, or the EDPB in its planned Guidance on the processing of health data for scientific research, urging the EDPB to revisit its recent Guidance on COVID-19 research

Zemrak, F., Raisi-Estabragh, Z., Khanji, M. Y., Mohiddin, S. A., Bruder, O., Wagner, A., ... & Nothnagel, D. (2020). "Left Ventricular Hypertrabeculation Is Not Associated With Cardiovascular Morbity or Mortality: Insights From the Eurocmr Registry." Frontiers in cardiovascular medicine, 7, 158.

The study assessed the association of LV trabeculation extent with cardiovascular morbidity and all-cause mortality in patients undergoing clinical cardiac magnetic resonance (CMR) scans across 57 European centers from the EuroCMR registry

Raisi‐Estabragh, Z., Biasiolli, L., Cooper, J., Aung, N., Fung, K., Paiva, J. M., ... & Rayner, J. J. (2020). "Poor bone quality is associated with greater arterial stiffness: insights from the UK Biobank." Journal of Bone and Mineral Research.

The article considers the potential mediating effect of a range of blood biomarkers and cardiometabolic morbidities and evaluates differential relationships by sex, menopause status, smoking, diabetes, and obesity, and considers whether associations with arterial compliance explained association of SOS with ischemic cardiovascular outcomes

Lekadir, K., Leiner, T., Young, A. A., & Petersen, S. E. (2020). Current and Future Role of Artificial Intelligence in Cardiac Imaging. Frontiers in Cardiovascular Medicine, 7.

This editorial provides a perspective of current and potential future role of AI in cardiac imaging

Zhuang, X., Xu, J., Luo, X., Chen, C., Ouyang, C., Rueckert, D., ... & Liu, Y. (2020). Cardiac segmentation on late gadolinium enhancement MRI: a benchmark study from multi-sequence cardiac MR segmentation challenge. arXiv preprint arXiv:2006.12434.

This paper presents the selective results from the Multi-Sequence Cardiac MR (MS-CMR) Segmentation challenge, in conjunction with MICCAI 2019.

Devriendt, T., Shabani, M., & Borry, P. (2020). "Data sharing platforms and the academic evaluation system". EMBO reports, e50690.

The article describes current efforts for Open Science and making it part of the academic evaluation system.

Raisi-Estabragh, Z., Cooper, J., Judge, R., Khanji, M. Y., Munroe, P. B., Cooper, C., ... & Petersen, S. E. (2020). "Age, sex and disease-specific associations between resting heart rate and cardiovascular mortality in the UK BIOBANK." PloS one, 15(5), e0233898.

The article defines the sex, age, and disease-specific associations of resting heart rate (RHR) with cardiovascular and mortality outcomes in 502,534 individuals from the UK Biobank over 7–12 years of prospective follow-up

Raisi-Estabragh, Z., Kenawy, A. A., Aung, N., Cooper, J., Munroe, P. B., Harvey, N. C., ... & Khanji, M. Y. (2020). "Variation in left ventricular cardiac magnetic resonance normal reference ranges: systematic review and meta-analysis." European Heart Journal-Cardiovascular Imaging.

The article determines population-related and technical sources of variation in cardiac magnetic resonance (CMR) reference ranges for left ventricular (LV) quantification through a formal systematic review and meta-analysis

Raisi-Estabragh, Z., Izquierdo, C., Campello, V. M., Martin-Isla, C., Jaggi, A., Harvey, N. C., ... & Petersen, S. E. (2020). "Cardiac magnetic resonance radiomics: basic principles and clinical perspectives." European Heart Journal-Cardiovascular Imaging, 21(4), 349-356

The article provides an overview of radiomics concepts for clinicians, with particular consideration of application to CMR, and reviews existing literature on CMR radiomics, discusses challenges, and considers directions for future work

Martin-Isla, C., Campello, V. M., Izquierdo, C., Raisi-Estabragh, Z., Baeßler, B., Petersen, S. E., & Lekadir, K. (2020). "Image-based cardiac diagnosis with machine learning: A review." Frontiers in Cardiovascular Medicine, 7, 1.

Cardiac imaging plays an important role in the diagnosis of cardiovascular disease (CVD). Until now, its role has been limited to visual and quantitative assessment of cardiac structure and function. However, with the advent of big data and machine learning, new opportunities are emerging to build artificial intelligence tools that will directly assist the clinician in the diagnosis of CVDs. This paper presents a thorough review of recent works in this field and provide the reader with a detailed presentation of the machine learning methods that can be further exploited to enable more automated, precise and early diagnosis of most CVDs.

Campello, V. M., Martín-Isla, C., Izquierdo, C., Petersen, S. E., Ballester, M. A. G., & Lekadir, K. (2019, October). "Combining Multi-Sequence and Synthetic Images for Improved Segmentation of Late Gadolinium Enhancement Cardiac MRI." In International Workshop on Statistical Atlases and Computational Models of the Heart (pp. 290-299). Springer, Cham.

Accurate segmentation of the cardiac boundaries in late gadolinium enhancement magnetic resonance images (LGE-MRI) is a fundamental step for accurate quantification of scar tissue. However, while there are many solutions for automatic cardiac segmentation of cine images, the presence of scar tissue can make the correct delineation of the myocardium in LGE-MRI challenging even for human experts. As part of the Multi-Sequence Cardiac MR Segmentation Challenge, we propose a solution for LGE-MRI segmentation based on two components. First, a generative adversarial network is trained for the task of modality-to-modality translation between cine and LGE-MRI sequences to obtain extra synthetic images for both modalities. Second, a deep learning model is trained for segmentation with different combinations of original, augmented and synthetic sequences. Our results based on three magnetic resonance sequences (LGE, bSSFP and T2) from 45 different patients show that the multi-sequence model training integrating synthetic images and data augmentation improves in the segmentation over conventional training with real datasets. In conclusion, the accuracy of the segmentation of LGE-MRI images can be improved by using complementary information provided by non-contrast MRI sequences.

Jagodzinski, A., Johansen, C., Koch-Gromus, U., Aarabi, G., Adam, G., Anders, S., ... & Betz, C. S. (2019). "Rationale and Design of the Hamburg City Health Study." European journal of epidemiology, 1-13.

The Hamburg City Health Study (HCHS) is a large, prospective, long-term, population-based cohort study and a unique research platform and network to obtain substantial knowledge about several important risk and prognostic factors in major chronic diseases. A random sample of 45,000 participants between 45 and 74 years of age from the general population of Hamburg, Germany, are taking part in an extensive baseline assessment at one dedicated study center. Participants undergo 13 validated and 5 novel examinations primarily targeting major organ system function and structures including extensive imaging examinations. The protocol includes validate self-reports via questionnaires regarding lifestyle and environmental conditions, dietary habits, physical condition and activity, sexual dysfunction, professional life, psychosocial context and burden, quality of life, digital media use, occupational, medical and family history as well as healthcare utilization. The assessment is completed by genomic and proteomic characterization. Beyond the identification of classical risk factors for major chronic diseases and survivorship, the core intention is to gather valid prevalence and incidence, and to develop complex models predicting health outcomes based on a multitude of examination data, imaging, biomarker, psychosocial and behavioral assessments. Participants at risk for coronary artery disease, atrial fibrillation, heart failure, stroke and dementia are invited for a visit to conduct an additional MRI examination of either heart or brain. Endpoint assessment of the overall sample will be completed through repeated follow-up examinations and surveys as well as related individual routine data from involved health and pension insurances. The study is targeting the complex relationship between biologic and psychosocial risk and resilience factors, chronic disease, health care use, survivorship and health as well as favorable and bad prognosis within a unique, large-scale long-term assessment with the perspective of further examinations after 6 years in a representative European metropolitan population.

Cetin, I., Petersen, S. E., Napel, S., Camara, O., Ballester, M. Á. G., & Lekadir, K. (2019, April). "A radiomics approach to analyze cardiac alterations in hypertension." In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) (pp. 640-643). IEEE.

Hypertension is a medical condition that is well-established as a risk factor for many major diseases. For example, it can cause alterations in the cardiac structure and function over time that can lead to heart related morbidity and mortality. However, at the subclinical stage, these changes are subtle and cannot be easily captured using conventional cardiovascular indices calculated from clinical cardiac imaging. In this paper, we describe a radiomics approach for identifying intermediate imaging phenotypes associated with hypertension. The method combines feature selection and machine learning techniques to identify the most subtle as well as complex structural and tissue changes in hypertensive subgroups as compared to healthy individuals. Validation based on a sample of asymptomatic hearts that include both hypertensive and non-hypertensive cases demonstrate that the proposed radiomics model is capable of detecting intensity and textural changes well beyond the capabilities of conventional imaging phenotypes, indicating its potential for improved understanding of the longitudinal effects of hypertension on cardiovascular health and disease.

Baker, D. B., Knoppers, B. M., Phillips, M., van Enckevort, D., Kaufmann, P., Lochmuller, H., & Taruscio, D. (2018). "Privacy-preserving linkage of genomic and clinical data sets." IEEE/ACM transactions on computational biology and bioinformatics, 16(4), 1342-1348.

The capacity to link records associated with the same individual across data sets is a key challenge for data-driven research. The challenge is exacerbated by the potential inclusion of both genomic and clinical data in data sets that may span multiple legal jurisdictions, and by the need to enable re-identification in limited circumstances. Privacy-Preserving Record Linkage (PPRL) methods address these challenges. In 2016, the Interdisciplinary Committee of the International Rare Diseases Research Consortium (IRDiRC) launched a task team to explore approaches to PPRL. The task team is a collaboration with the Global Alliance for Genomics and Health (GA4GH) Regulatory and Ethics and Data Security Work Streams, and aims to prepare policy and technology standards to enable highly reliable linking of records associated with the same individual without disclosing their identity except under conditions in which the use of the data has led to information of importance to the individual's safety or health, and applicable law allows or requires the return of results. The PPRL Task Force has examined the ethico-legal requirements, constraints, and implications of PPRL, and has applied this knowledge to the exploration of technology methods and approaches to PPRL. This paper reports and justifies the findings and recommendations thus far.

Nguyen, M. T., Goldblatt, J., Isasi, R., Jagut, M., Jonker, A. H., Kaufmann, P., ... & Tassé, A. M. (2019). "Model consent clauses for rare disease research." BMC medical ethics, 20(1), 55.

Rare Disease research has seen tremendous advancements over the last decades, with the development of new technologies, various global collaborative efforts and improved data sharing. To maximize the impact of and to further build on these developments, there is a need for model consent clauses for rare diseases research, in order to improve data interoperability, to meet the informational needs of participants, and to ensure proper ethical and legal use of data sources and participants’ overall protection. A global Task Force was set up to develop model consent clauses specific to rare diseases research, that are comprehensive, harmonized, readily accessible, and internationally applicable, facilitating the recruitment and consent of rare disease research participants around the world. Existing consent forms and notices of consent were analyzed and classified under different consent themes, which were used as background to develop the model consent clauses. The IRDiRC-GA4GH MCC Task Force met in September 2018, to discuss and design model consent clauses. Based on analyzed consent forms, they listed generic core elements and designed the following rare disease research specific core elements; Rare Disease Research Introductory Clause, Familial Participation, Audio/Visual Imaging, Collecting, storing, sharing of rare disease data, Recontact for matching, Data Linkage, Return of Results to Family Members, Incapacity/Death, and Benefits.


"Call for cardiovascular scientists to contribute data to multinational platform" (Press release, 06/04/2021)

This press release announces the launch of the public euCanSHare platform.

"GA4GH GDPR Brief: The Concept of Personal Data in the GDPR"

This brief article by Alexander Bernier (McGill University’s Centre of Genomics and Policy) clarifies the concept of personal data in the context f the GDPR, identifiability and re-identification risks

"La maquina que predice quien enfermara" (El Pais, 27 November 2019)

A newspaper article with interview with the PC (SPANISH)

"euCanSHare: EU-Canada joint data platform to facilitate multi-study cardiovascular research" (Press release, 12/12/2018)

A press release annoucing project inception, rationale and mission, produced and circulated right after the project kick-off meeting (Brussels, 11 December 2018)

Public Deliverables

Deliverable 4.4 - Bioinformatics Toolbox (30/11/2020)

This deliverable demonstrates the applicability of a bioinformatical tool (part of a larger toolbox) that can either analyse external data through an upload mechanism or offer the automatic analysis of internal server-housed data

Deliverable 4.3 - Cardiac image analyser (30/11/2020)

This deliverable presents the Cardiac Image Analyzer, the first open-source image analysis platform for cardiac magnetic resonance images (CMR)

Deliverable 3.4 - Guidelines and protocol for data deposition (30/11/2020)

This deliverable describes the guidelines and protocols for medical data deposition for the Euro-BioImaging medical imaging archive

Deliverable 3.2 - Data Management Plan v2 (30/11/2020)

This deliverable ummarizes the required updates on the euCanSHare’s Data Management Plan, after the first half of the project execution

Deliverable 2.2 - Data distribution protocols and interfaces (30/11/2020)

This deliverable reviews the efforts dedicated to the development and implementation of interoperability channels among euCanSHare components, as well as the creation of uniformed connections with external consumers and services

Deliverable 1.3 - Comparative cross-mapping table detailing which participating cohorts are compliant with euCanSHare requirements (30/11/2020)

This deliverable provides a holistic review of the consent materials, governance documentation, and data use permissions of 31 participating euCanSHare cohorts

Deliverable 1.2 - Policy "Points to Consider" tool to guide research projects, policy makers (30/11/2020)

This deliverable reports an analysis of the ethico-legal requirements enshrined in data privacy law and research ethics guidance in Canada and the European Union

Deliverable 4.1 - Opal software integration to euCanSHare (30/11/2019)

This deliverable reports on the integration of the OBiBa software suite (Opal, Mica and Agate) and its harmonization and cataloguing resources (harmonization guidelines and metadata standards) in euCanSHare

Deliverable 2.1 - Initial Infrastructure framework and documentation (30/11/2019)

This deliverable describes the technological foundations of the cloud-based euCanSHare data portal, including the computational infrastructure for data management and analysis, the authentication and authorization system and user interfaces

Deliverable 3.1 - Data management plan (31/05/2019)

This deliverable describes data handled within euCanSHare and the data handling model, including data collection, secure long-term storage, integration and interoperability, accessibility and exploitation

Deliverable 6.1 - Project website (28/02/2019)

This deliverable reports the process of design, collective refinement and implementation of euCanSHare public website, along with up-to-date images and infographics