During the 2nd period, from 18 to 36 months, the REALMENT project has moved forward largely according to plan.
Real World Data: We continued to curate and harmonize multimorbidity and pharmacological data across cohorts for specific projects, see below.
Infrastructure and methods: We expanded our infrastructure for algorithm development based on partners secure data systems, and started adding Danish, Estonian and Swedish partners to the Dutch and Norwegian infrastructures. We coordinate software container solutions for integrated distributed analyses of Nordic data overall. The UK and Estonian partners have initiated initiatives on electronic data mining approaches.
Main results and achievements:
We published an overview article on how Real-World Data Can Facilitate the Development of Precision Medicine Treatment in Psychiatry.
Algorithm development: We have further tested machine learning (ML) models, such as regression and random forest algorithms, alongside deep learning models, specifically artificial feed forward neural networks, on both GWAS-informed and raw genotype patient-level data. We continue to work on the Finemap-MiXeR, which determines the causal single nucleotide polymorphisms (SNPs) associated with a trait at a given locus.
This work will form the basis for better models to predict treatment trajectories.
Gene discoveries: We expanded our previous GWAS on treatment response and meta-analysed with a Finnish sample. The results identified a single genome-wide significant locus tagging the GATA4 gene, which is described as a transcriptional regulator of genes involved in antipsychotic pharmacokinetics and thus a potentially actionable target. Work is still on-going to identify genetic variants associated with side-effects from SSRIs, involving several partners.
Genetics of trajectories: We initiated the work on the identification of genetic factors associated with treatment trajectories and are developing a framework to systematically evaluate the familial and genetic components influencing pharmacological trajectories. We saw an effect of age on onset on the probability of transitioning to clozapine, with an increased chance for early onset patients. Also, preliminary results have shown a male specific effect of family history of schizophrenia on the eventual prescription of clozapine. We are extending these analyses to other outcomes and medications while developing a framework to systematically evaluate the familial and genetic components influencing pharmacological trajectories.
Treatment response:
Prediction: We have worked on new approaches to increase the predictive power of personalized polygenic risk scores. We found that polygenic risk score for schizophrenia was significantly positively associated with prescribed (standardized) antipsychotic dose and antipsychotic polypharmacy with direction effects the same all cross-border five cohorts tested. Further, we have expanded the work to included antidepressants, and have now a paper in review describing these findings.
4MENT management platform: We further developed the 4MENT clinical management platform by improving the design based on stakeholder input and incorporate algorithms for multimodal prediction of treatment-resistance schizophrenia, including polygenic risk scores and clinical variables as input. We also initiated the ethical, data protection and legal framework for the platform design;
Cooordination, dissemination and exploitation: The 2nd stakeholder forum was held at the 3rd annual meeting of the project. Finally, the website has updated with lay summaries and the project is continuously being presented at several conferences and events including the world congress of psychiatric genetics, the European College Neuropsychopharmacology (ECNP) and Psychiatric Genetics Consortium (PGC) lab meeting. Flyers on the project have been distributed at several places.
Some deliverables have been postponed due to our initial issues hiring personnel. However, we have now the personnel available, and are catching up with the activities.
