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)
Période du rapport: 2021-06-01 au 2022-11-30
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.
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.