Periodic Reporting for period 1 - REALMENT (Using real-world big data from eHealth, biobanks and national registries, integrated with clinical trial data to improve outcome of severe mental disorders)
Berichtszeitraum: 2021-06-01 bis 2022-11-30
Mental disorders are amongst the largest disease groups and represent a major public health concern, posing a large financial burden on the European health care system. The total cost of mental disorders was recently estimated at more than 4% of GDP – or over €600 billion – in Europe. Overall, individuals with severe mental disorders have not benefited from the general advances in health care in the last decades, largely due to a lack of progress in pharmacological treatment and disease management. Moreover, the presence of multimorbidities, including both comorbid mental and somatic diseases, adds to the suffering and large reduction in quality of life (QoL) experienced by this patient group. The main aim of REALMENT is to unleash the potential of clinical trial data in combination with large psychopharmacological Real-World Data (electronic Health Records, health registries, genotyped biobanks) applying novel Artificial Intelligence and Machine Learning technology to enable a personalized approach (‘precision psychiatry’) for clinical application in a Clinical Management Platform (4MENT). We will exploit our unique access to large-scale available Real World Data samples derived from cohorts, medical records and the unique Nordic biobanks and registries, where lifespan diagnostic and prescription information is available from each individual. We will apply cutting-edge “big data” analytical approaches to Real World Data from Nordic, Baltic, British, Dutch and Italian cohorts to: i) identify and validate genetic markers of treatment outcome and multimorbidities, ii) develop tools suitable for prediction and stratification of treatment and iii) identify sub-populations that would benefit from preventive strategies.
During the first 18 months, the REALMENT project has moved forward largely according to plan.
Real World Data: We started to curate and harmonize multimorbidity and pharmacological data across cohorts and despite not yet fully finalized, we have used these data in various projects, see below.
Infrastructure and methods: We are building our infrastructure for algorithm development based on partners secure data systems, starting with the Dutch and Norwegian partners. Our infrastructure will be coordinated with the software container solutions for integrated distributed analyses of Nordic data overall. Several partners are involved in ongoing initiatives on electronic data mining approaches - this line of work is awaiting hiring of people.
Main results and achievements:
Algorithm development: We have specifically tested different clusters corresponding to different scenarios of structured genetic heterogeneity. Discoveries from Genome-wide association studies (GWAS) often contain large clusters of highly correlated genetic variants, which makes them hard to interpret and use in prediction. To address this challenge, we work on a new method, the Finemap-MiXeR, determining the causal single nucleotide polymorphisms (SNPs) associated with a trait at a given locus.
We have worked on new approaches to increase the predictive power of personalized polygenic risk scores. To improve the biological interpretation of the findings from GWAS, we developed the GSA-MiXeR analytical tool for gene-set analysis (GSA), which allows the quantification of partitioned heritability and fold enrichment for small gene-sets.
This work will form the basis for better models to predict treatment trajectories.
Treatment response: We investigated clozapine prescription patterns. In parallel, we performed the first GWAS of treatment-resistant depression based on samples from Sweden, Finland, and Estonia. Likewise, we used genetic information from Norwegian, Icelandic and Finnish samples to predict dose requirements of various antipsychotics. Preliminary results indicate that genetic burden of schizophrenia is associated with higher dose requirements of different antipsychotics. In parallel, we are performing GWAS of dose requirements across antipsychotics within the same cohorts.
University of Oslo (UiO) has identified a novel genetic variant associated with treatment resistant schizophrenia from GWAS in the longitudinal Norwegian therapeutic drug monitoring sample. Based on this sample with clozapine (defined as treatment resistant) and risperidone (defined as treatment responsive) users, findings from Cardiff University (CAR) are attempted to be replicated.
The first GWAS of early switching has been done in UK Biobank, Estonian Biobank, deCODE (Iceland) and iPSYCH (Denmark) and meta-analyzed.
We did a follow-up study in Norwy that showed a relationship between schizophrenia Polygenic Risk Score and maximum clozapine dose and expanded to the analysis to other antipsychotics. Icelandic and Finnish samples will attempt validation of these results.
4MENT management platform: We took the first steps to building the 4MENT clinical management platform through design developments in an initial interface (beta version).
Cooordination, dissemination and exploitation: The project has put together a well-functioning coordination team including routines, management structures and tools to support the efficient running of a highly interconnected project. We wrote a dissemination, exploitation & communications plan, and the patient association GAMIAN-Europe has been recruited as a dissemination partner (subcontract). The first stakeholder forum has been held at the 2nd annual meeting of the project. Finally, the website has been launched along with the twitter account and the project has been presented at several conferences and events.
Several partners have experienced difficulties in hiring personnel. We also experienced other COVID-19 related delays, such as access to Real World Data from some Health Registries and clinical trials. Thus, the initial data freeze is still not complete, but we have started with first set of analyses and establishing the infrastructure. Thus, the project has seen considerable progress despite these challenges.
Real World Data: We started to curate and harmonize multimorbidity and pharmacological data across cohorts and despite not yet fully finalized, we have used these data in various projects, see below.
Infrastructure and methods: We are building our infrastructure for algorithm development based on partners secure data systems, starting with the Dutch and Norwegian partners. Our infrastructure will be coordinated with the software container solutions for integrated distributed analyses of Nordic data overall. Several partners are involved in ongoing initiatives on electronic data mining approaches - this line of work is awaiting hiring of people.
Main results and achievements:
Algorithm development: We have specifically tested different clusters corresponding to different scenarios of structured genetic heterogeneity. Discoveries from Genome-wide association studies (GWAS) often contain large clusters of highly correlated genetic variants, which makes them hard to interpret and use in prediction. To address this challenge, we work on a new method, the Finemap-MiXeR, determining the causal single nucleotide polymorphisms (SNPs) associated with a trait at a given locus.
We have worked on new approaches to increase the predictive power of personalized polygenic risk scores. To improve the biological interpretation of the findings from GWAS, we developed the GSA-MiXeR analytical tool for gene-set analysis (GSA), which allows the quantification of partitioned heritability and fold enrichment for small gene-sets.
This work will form the basis for better models to predict treatment trajectories.
Treatment response: We investigated clozapine prescription patterns. In parallel, we performed the first GWAS of treatment-resistant depression based on samples from Sweden, Finland, and Estonia. Likewise, we used genetic information from Norwegian, Icelandic and Finnish samples to predict dose requirements of various antipsychotics. Preliminary results indicate that genetic burden of schizophrenia is associated with higher dose requirements of different antipsychotics. In parallel, we are performing GWAS of dose requirements across antipsychotics within the same cohorts.
University of Oslo (UiO) has identified a novel genetic variant associated with treatment resistant schizophrenia from GWAS in the longitudinal Norwegian therapeutic drug monitoring sample. Based on this sample with clozapine (defined as treatment resistant) and risperidone (defined as treatment responsive) users, findings from Cardiff University (CAR) are attempted to be replicated.
The first GWAS of early switching has been done in UK Biobank, Estonian Biobank, deCODE (Iceland) and iPSYCH (Denmark) and meta-analyzed.
We did a follow-up study in Norwy that showed a relationship between schizophrenia Polygenic Risk Score and maximum clozapine dose and expanded to the analysis to other antipsychotics. Icelandic and Finnish samples will attempt validation of these results.
4MENT management platform: We took the first steps to building the 4MENT clinical management platform through design developments in an initial interface (beta version).
Cooordination, dissemination and exploitation: The project has put together a well-functioning coordination team including routines, management structures and tools to support the efficient running of a highly interconnected project. We wrote a dissemination, exploitation & communications plan, and the patient association GAMIAN-Europe has been recruited as a dissemination partner (subcontract). The first stakeholder forum has been held at the 2nd annual meeting of the project. Finally, the website has been launched along with the twitter account and the project has been presented at several conferences and events.
Several partners have experienced difficulties in hiring personnel. We also experienced other COVID-19 related delays, such as access to Real World Data from some Health Registries and clinical trials. Thus, the initial data freeze is still not complete, but we have started with first set of analyses and establishing the infrastructure. Thus, the project has seen considerable progress despite these challenges.
We expect to make large progress on identifying predictors of treatment response of major psychopharmacological agents applying real world data from the large biobanks and health registry data. This will enable us to perform a series of GWAS of phenotypes (treatment outcome), not yet possible with other types of data. As an example, we have performed the first GWAS of treatment-resistant depression.
A long-range sequencing project is in planning taking advantage of several partners’ samples. We expect to find variants involved in drug metabolism in patients, useful for prediction of drug response and side effects.
REALMENT will continue to identify genetic variation associated with treatment outcome and multimorbidities building on development of suitable analytical tools, including prediction and stratification algorithms. We will validate our discoveries in independent samples, both Real-World Data and clinical trial data, and develop a management platform for improved outcome and quality of life for individuals with mental disorders.
A long-range sequencing project is in planning taking advantage of several partners’ samples. We expect to find variants involved in drug metabolism in patients, useful for prediction of drug response and side effects.
REALMENT will continue to identify genetic variation associated with treatment outcome and multimorbidities building on development of suitable analytical tools, including prediction and stratification algorithms. We will validate our discoveries in independent samples, both Real-World Data and clinical trial data, and develop a management platform for improved outcome and quality of life for individuals with mental disorders.