Periodic Reporting for period 1 - CoMorMent (Predicting comorbid cardiovascular disease in individuals with mental disorder by decoding disease mechanisms)
Reporting period: 2020-01-01 to 2021-06-30
Severe mental disorders are major public health concerns and pose a considerable financial burden on the European Health Care systems. At the individual level, human suffering is enormous and associated with a substantial reduction in quality of life, due to the clinical manifestations of the disease (depression/psychosis/mania and cognitive dysfunction) and loss of financial stability. In fact, people with severe mental disorders in Europe live 15-20 years less than the rest of the population, mainly due to comorbid cardiovascular diseases (CVD). Recent results indicate that overlapping genetic risk factors and brain mechanisms between mental illness and behavioural traits are associated with a lifestyle leading to CVD.
The main objective of the CoMorMent project is to find new paths for clinical management of mental disorders and CVD co-morbidities through identification and validation of biomarkers.
We are addressing this through three different sub-objectives:
1. To identify genetic variants associated with different trajectories of the co-morbitidity of CVD and mental disorders comorbidity. (Discovery).
2. To identify genetic variants associated with lifestyle and behavioural factors and determine their correlation with CVD and mental disorders comorbidity, and role of non-inherited factors (Nature and Nurture).
3. To identify the causative mechanisms of the gene variants and develop prediction tools for targeted prevention in a personalized medicine framework (Validation and Biomarkers).
The main objective of the CoMorMent project is to find new paths for clinical management of mental disorders and CVD co-morbidities through identification and validation of biomarkers.
We are addressing this through three different sub-objectives:
1. To identify genetic variants associated with different trajectories of the co-morbitidity of CVD and mental disorders comorbidity. (Discovery).
2. To identify genetic variants associated with lifestyle and behavioural factors and determine their correlation with CVD and mental disorders comorbidity, and role of non-inherited factors (Nature and Nurture).
3. To identify the causative mechanisms of the gene variants and develop prediction tools for targeted prevention in a personalized medicine framework (Validation and Biomarkers).
During the first 18 months, the CoMorMent project has moved forward according to plan.
Data: AMRA has processed ~42,000 individuals from UK biobank to quantify muscle fat infiltration and ectopic fat distribution. 200k Norwegian and Danish chromosomes have been long-range-phased and common and rare variants imputed into the phased data. Partners have gathered the most recent meta-analysis data (summary statistics) for educational attainment, cognition and psychiatric disorders to estimate the direct and indirect effects of the polygenic risk scores generated for the respective phenotypes.
Data harmonization: Harmonization of cardiovascular and mental disorder diagnoses was achieved across diagnoses systems. UoI has prepared nationwide data on all psychiatric diagnoses, psychotropic medication use, and CVD diagnoses from the population registers to be used for validation/pooled analysis.
Data analysis pipelines: We successfully developed the first test case for distributed container-based analysis with subsequent testing and validation locally with several partners. We have designed a pilot version of the desired harmonization protocol for meta-data and a pipeline for genotype imputation. The analytical pipeline for body – brain imaging phenotypes has been prepared, and final analysis is ready to start final analyses when data are released from UK Biobank. We have begun setting up the analytical framework for combining output public data with open compendia of cardiac drugs and targets. UTARTU has further extracted loss-of-function and missense variants in psychiatric drug targets. Further, the Danish partner has implemented their analytical methodology for trajectories of mental illness, and started coordination across Nordic registry data samples. Initial testing and review of the pharmacogenetics pipeline was carried out, testing and validating on clinical samples was initiated.
Main results and achievements: Meta-analysis has been carried out for several mental and behavioural disorders. using our developed pipeline for analysis of the impact of the non-transmitted alleles. Preliminary data are promising for the meta-analysis of rare variants as well as for the impact of non-transmitted alleles. These associations are to be tested in the Estonian and Swedish samples before publishing the findings. We have preliminary data concerning genetic overlap between SCZ and CVD, with promising results. Partners have furthermore contributed to meta-analysis papers. Preliminary data obtained in collaboration on the pharmacogenetics pipeline show promising results for prediction of response vs. non-response of antipsychotic.
We have several publications about genetic overlap between BIP and CVD, as well as CVD and mental traits.
We prepared a manuscript using UiO-acquired brain and body magnetic resonance imaging (MRI) data. Finally, we have published baseline work (Gurholt et al. Translational Psychiatry (2021) 11:295) using publicly available brain and body MRI data from the UK Biobank.
Cooordination, dissemination and exploitation: We successfully delivered a well-functioning coordination team, routines, management structure, management tools, support and meeting structure to support the efficient running of a highly interconnected project with implementation of activities and research in several countries and by several partners. A dissemination, Exploitation & Communications plan has been written and agreed upon. The webside was populated and launched along with the twitter account and the first newsletter has been distributed. The stakeholder forum has 13 members and has had two meetings where they provided input to the project.
Data: AMRA has processed ~42,000 individuals from UK biobank to quantify muscle fat infiltration and ectopic fat distribution. 200k Norwegian and Danish chromosomes have been long-range-phased and common and rare variants imputed into the phased data. Partners have gathered the most recent meta-analysis data (summary statistics) for educational attainment, cognition and psychiatric disorders to estimate the direct and indirect effects of the polygenic risk scores generated for the respective phenotypes.
Data harmonization: Harmonization of cardiovascular and mental disorder diagnoses was achieved across diagnoses systems. UoI has prepared nationwide data on all psychiatric diagnoses, psychotropic medication use, and CVD diagnoses from the population registers to be used for validation/pooled analysis.
Data analysis pipelines: We successfully developed the first test case for distributed container-based analysis with subsequent testing and validation locally with several partners. We have designed a pilot version of the desired harmonization protocol for meta-data and a pipeline for genotype imputation. The analytical pipeline for body – brain imaging phenotypes has been prepared, and final analysis is ready to start final analyses when data are released from UK Biobank. We have begun setting up the analytical framework for combining output public data with open compendia of cardiac drugs and targets. UTARTU has further extracted loss-of-function and missense variants in psychiatric drug targets. Further, the Danish partner has implemented their analytical methodology for trajectories of mental illness, and started coordination across Nordic registry data samples. Initial testing and review of the pharmacogenetics pipeline was carried out, testing and validating on clinical samples was initiated.
Main results and achievements: Meta-analysis has been carried out for several mental and behavioural disorders. using our developed pipeline for analysis of the impact of the non-transmitted alleles. Preliminary data are promising for the meta-analysis of rare variants as well as for the impact of non-transmitted alleles. These associations are to be tested in the Estonian and Swedish samples before publishing the findings. We have preliminary data concerning genetic overlap between SCZ and CVD, with promising results. Partners have furthermore contributed to meta-analysis papers. Preliminary data obtained in collaboration on the pharmacogenetics pipeline show promising results for prediction of response vs. non-response of antipsychotic.
We have several publications about genetic overlap between BIP and CVD, as well as CVD and mental traits.
We prepared a manuscript using UiO-acquired brain and body magnetic resonance imaging (MRI) data. Finally, we have published baseline work (Gurholt et al. Translational Psychiatry (2021) 11:295) using publicly available brain and body MRI data from the UK Biobank.
Cooordination, dissemination and exploitation: We successfully delivered a well-functioning coordination team, routines, management structure, management tools, support and meeting structure to support the efficient running of a highly interconnected project with implementation of activities and research in several countries and by several partners. A dissemination, Exploitation & Communications plan has been written and agreed upon. The webside was populated and launched along with the twitter account and the first newsletter has been distributed. The stakeholder forum has 13 members and has had two meetings where they provided input to the project.
In our published work on brain-body imaging correlations, we showed the first large-scaled linkage of normally varying anthropometric (e.g. body mass index) and body fat distributions from body imaging to brain structure in a largely healthy population.
We have shown overlapping genetic structure of bipolar disorder and cardiovascular disease and mental traits and cardiovascular disease.
CoMorMent will continue to identify genetic, brain and body biomarkers and pathological mechanisms of comorbid CVD in mental disorders and its relationship to behavior and unhealthy lifestil. We will develop tools for prediction, early diagnosis and better disease monitoring. This knowledge is critical for development of prevention and better treatment, which will form the basis for a new approach in clinical studies with improved quality of life for individuals living with mental disorders.
We have shown overlapping genetic structure of bipolar disorder and cardiovascular disease and mental traits and cardiovascular disease.
CoMorMent will continue to identify genetic, brain and body biomarkers and pathological mechanisms of comorbid CVD in mental disorders and its relationship to behavior and unhealthy lifestil. We will develop tools for prediction, early diagnosis and better disease monitoring. This knowledge is critical for development of prevention and better treatment, which will form the basis for a new approach in clinical studies with improved quality of life for individuals living with mental disorders.