Among the different disease conditions that affect the European and global population, mental disorders are increasingly common and 15% or the population may be affected during their lifetime. The total cost of mental disorders was estimated at over € 600 billion across the EU-28 countries in 2018, with patients and families experiencing serious shortcomings such as unemployment, reduced life opportunities, social underachievement, stigma and reduced life expectancy.

Adding to this already heavy burden, mental disorders are often associated with additional multi-morbidities such as additional psychiatric diagnoses or somatic diseases such as CVD, arthritis, chronic pain, asthma, lung disease, cancer or other health problems, contributing an 40-50% extra in costs per year for the European health care system.

Likewise, comorbidities and poorer physical health account for 60% of the excess mortality – amounting to a 15-20-year shorter lifespan – observed in patients of mental disorders. People with co-morbid mental and somatic diseases are also among those with the highest risk of safety incidents.

Even small benefits in mental health, therefore, have the potential to translate into major financial savings and improved treatment of multi-morbidities, materially enhancing the quality of life of patients.

To rectify the gap in mental disorder treatment and management, the European Commission, World Health Organization and  has developed a set of activities and OECD have all emphasised the need to improve the knowledge on mental health by collecting data at the population level and to perform epidemiological research to identify the causes behind mental health.

REALMENT is perfectly aligned with this goal. We will collect, analyse and integrate real-world data relating to mental disorders and associated multi-morbidities from previous and ongoing research projects, industry sponsored clinical trials, clinical health records, population registries and large genetic cohorts.

By encouraging the use of available real-world data resources to advance the development of personalised medicine, REALMENT will provide the basis for improved prevention, treatment, follow-up and other policies, while delivering structures and resources that respect patient privacy and patient integrity.

How REALMENT's impact aligns with Europe's strategic health care goals


Use of multi-disciplinary multi-source real-world data to advance clinical research on complex chronic conditions

REALMENT will use multi-disciplinary real-world data to enable the development of predictive and stratifying algorithms for disease, trajectory and drug response, opening up new opportunities for clinical research on these complex chronic conditions. Through this approach, REALMENT will  pave the path for more smooth data and infrastructure-based collaborations across Europe for other chronic contribute to advance knowledge of severe mental diseases and clinical management/treatment diseases and their respective management.

Use of real-world data from disease-specific professional societies/associations, by health authorities to understand safety, quality and effectiveness of therapies

The real-world data collected and integrated in REALMENT will enable the identification of disease progression and clinical trajectories of patients with severe mental conditions in extended time frames – ultimately along the whole lifetime of a patient. Specifically, this will allow studying long-term effects and adverse effects of pharmacological treatments in psychiatric patients, including the effects of polypharmacy in multi-morbid patients.

Drawing upon data sources on an unprecedented scale will help to circumvent the major limitations of randomised controlled trials and improve the standards of care in terms of safety, quality and effectiveness of medication therapies and public health strategies.

By identifying genetic risk profiles that are associated with increased risk of multimorbidities in patients with mental disorders, we will develop prediction and management tools to reduce adverse effects, thereby helping patients on existing drugs, while enabling the trialling and development of new, improved drug regimens with less adverse effects.

Improving the clinical outcomes as well as quality of life of patients living with complex chronic conditions

The results of the project will enable improved understanding of long-term effects of pharmacological treatments and the development of better clinical strategies to reduce adverse effects for the patients, as well as preventing complications related to polypharmacy (namely to manage multi-morbidities). This will have a strong impact on the quality of life of psychiatric patients.

Furthermore, by developing advanced prediction and patient stratification tools, the project will open up opportunities for improving the clinical outcomes of patients who do not respond to pharmacological treatments – an estimated 30% of those suffering from schizophrenia, bipolar disorder and major depression. Identifying the non-responsive patients upfront will enable economic savings, while avoiding adverse effects derived from the administration of ineffective/unnecessary treatments. Finally, by facilitating the development of new treatment regimens tailored to these non-responding patients, the project will pave the way for individualised treatments and facilitate a paradigm change towards precision psychiatry.

Advancing the understanding of management of complex diseases, including the interdependence of comorbidities, thus underpinning evidence-based therapies and prognostic approaches

Currently, no biomarkers exist for the outcome of mental disorders, development of co-morbidities, drug response or efficacy. Building on the REALMENT biomarker discoveries and new machine-language-based algorithms, REALMENT will develop clinically relevant biomarkers for stratification of pharmacological treatment and prediction of adverse events and the development of comorbidities based on trajectory models.

Implementing the stratification and prediction tools based on measurable biomarkers, namely genotypes in combination with other response predictors (symptoms, disease history, cardiometabolic blood markers, BMI, etc), will significantly benefit the management of complex mental disorders. In combination with mapping information of multimorbid trajectories, a comprehensive tool for individualised management can be developed to guide physicians and integrate feedback from individual patients to improve the outcome, and to follow their individual progress.

Furthering the development of new technological tools and platforms for advanced data management

REALMENT will develop a series of novel ML and AI software algorithms for data capture, curation, amalgamation and integration in addition to risk factor discovery, prediction and stratification. By sharing the analytical code for academic use, following Open Science policy, we will enable other researchers to benefit from the REALMENT tool development, while IP is secured for commercial exploitation. We will develop a framework to enable expansion of the data platform with additional national secure servers. The experience obtained there will be shared with others through the REAL-WD Platform Ecosystem.

Contributing to the cross-border health data exchange and to the goals of the Digital Single Market

The REALMENT project will aggregate data from multiple sources at 10 countries and enable cross-border analysis, aiming to expand the data collection/sources and leveraging the models, tools and data platform (REAL-WD Platform) developed during the project – both through the sharing and use of the containers for distributed analysis between partners and through the sharing of code and analysis tools. Such scaling will be informed by the exploitation and dissemination activities during the project, which will include reaching out to relevant research and clinical communities beyond REALMENT to showcase the potential of REALMENT’s approach and use the lessons learned during the project to inform real-world deployment of the REALMENT health tools and platform in diverse contexts/countries.

Ongoing impacts

REALMENT will not only deliver the impacts expected under the terms of the grant, but will provide additional impacts in other areas:

Enhancing innovation and creating new market opportunities.

The innovation potential of REALMENT includes the discovery of additional causal factors (genes) in mental disorders, the translation of the scientific findings into clinical settings by improving algorithms based on ‘big data’ for disease and treatment stratification and prediction using real-world data, and the development of new, optimised treatments and management strategies including an outline for a management platform for bipolar, major depression and schizophrenia patients.

These objectives will provide tools for treatment and follow-up of patients suffering from severe mental disorders and co-morbidities and provide insights crucial for further applied research to enter into a commercial setting.

Strengthening competitiveness and growth of companies.

REALMENT includes a high-tech SME, CorTechs Labs Inc., a global pharmaceutical enterprise, Janssen, a CRO Smerud Medical Research International AS, and two large global enterprises, one based in Iceland, Islensk Erfdagreining ehf (deCODE genetics) and one in Norway, DNV GL. All of these companies will increase their competitiveness through exploitation of the results of the project.

Societal impacts – contribution to healthcare priorities within mental diseases and sustainability of health care systems.

The healthcare and societal costs of mental disorders are enormous. The total cost of mental disorders was estimated at more than 4% of GDP across the 28 European countries in 20183. Of these, € 190 billion reflects direct spending on health care, another € 170 billion is spent on social security programmes, while a further € 240 billion represents indirect costs to the labour market due to lower employment and productivity. Some of the specific impacts expected are:

  • Improved targeting of disease groups within randomised controlled trials for optimising treatment outcome. The algorithms developed in REALMENT can help stratify patients into defined categories based on genetic risk profiles.
  • Decreased number of hospitalisations and treatment-requiring actions. Due to optimised treatment dose and reduction of adverse effects through the obtained insights in disease management. 
  • Fewer relapse and less development of comorbidities. The REALMENT decision support tools will help prevent new psychotic and mood episodes. Comorbidities may be prevented by defining the individual risk and initiate preventive measures.
  • Unleashing the potential for precision medicine in psychiatry. Most of the research in this field has happened in somatic specialties such as cancer. By combining new tools, psychiatry will take a major step towards precision psychiatry.

Other substantial impacts

  • Training and mentoring of young research talents into scientific leadership positions. REALMENT will assign roles and responsibilities to young, promising scientists and provide an environment where they can reach their full potential. In addition, we will collaborate with the Marie Skłodowska-Curie COFUND-programme co-ordinated by University of Oslo (Scientia-Fellow II) and take full advantage of the transferable skills, leadership courses and Health Innovation School provided by Scientia-Fellow II to train and improve the future prospects and skills of young scientists.
  • Gender aspects – women in science. REALMENT aims to achieve a gender balanced structure and reach at least 40% scientists of either sex. The current team includes seven female and seven male senior scientists. We will promote recruitment of the under-represented gender, e.g. females in statistics, males in clinical psychology. In addition, we will develop a set of measures to promote the career of young researchers, in particular seminars to highlight the future possibilities for researchers in the field. These sessions will include practical advice on how to address working family life balance, a topic important to promote female scientists to leadership positions.
Published June 20, 2022 12:02 PM - Last modified June 20, 2022 12:04 PM