Unique genetic and risk-factor profiles in clusters of major depressive disorder-related multimorbidity trajectories
The heterogeneity and complexity of symptom presentation, comorbidities and genetic factors pose challenges to the identification of biological mechanisms underlying complex diseases. Current approaches used to identify biological subtypes of major depressive disorder (MDD) mainly focus on clinical...
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| Veröffentlicht in: | Nature communications Jg. 15; H. 1; S. 7190 - 18 |
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21.08.2024
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| Abstract | The heterogeneity and complexity of symptom presentation, comorbidities and genetic factors pose challenges to the identification of biological mechanisms underlying complex diseases. Current approaches used to identify biological subtypes of major depressive disorder (MDD) mainly focus on clinical characteristics that cannot be linked to specific biological models. Here, we examined multimorbidities to identify MDD subtypes with distinct genetic and non-genetic factors. We leveraged dynamic Bayesian network approaches to determine a minimal set of multimorbidities relevant to MDD and identified seven clusters of disease-burden trajectories throughout the lifespan among 1.2 million participants from cohorts in the UK, Finland, and Spain. The clusters had clear protective- and risk-factor profiles as well as age-specific clinical courses mainly driven by inflammatory processes, and a comprehensive map of heritability and genetic correlations among these clusters was revealed. Our results can guide the development of personalized treatments for MDD based on the unique genetic, clinical and non-genetic risk-factor profiles of patients.
Major depressive disorder is a heterogeneous condition with varied presentation of symptoms, comorbidities, and related genetic factors. This study aimed to identify clusters of major depressive disorder-related longitudinal multimorbidity trajectories and to characterize the clusters using genetic and risk factor data. |
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| AbstractList | The heterogeneity and complexity of symptom presentation, comorbidities and genetic factors pose challenges to the identification of biological mechanisms underlying complex diseases. Current approaches used to identify biological subtypes of major depressive disorder (MDD) mainly focus on clinical characteristics that cannot be linked to specific biological models. Here, we examined multimorbidities to identify MDD subtypes with distinct genetic and non-genetic factors. We leveraged dynamic Bayesian network approaches to determine a minimal set of multimorbidities relevant to MDD and identified seven clusters of disease-burden trajectories throughout the lifespan among 1.2 million participants from cohorts in the UK, Finland, and Spain. The clusters had clear protective- and risk-factor profiles as well as age-specific clinical courses mainly driven by inflammatory processes, and a comprehensive map of heritability and genetic correlations among these clusters was revealed. Our results can guide the development of personalized treatments for MDD based on the unique genetic, clinical and non-genetic risk-factor profiles of patients. The heterogeneity and complexity of symptom presentation, comorbidities and genetic factors pose challenges to the identification of biological mechanisms underlying complex diseases. Current approaches used to identify biological subtypes of major depressive disorder (MDD) mainly focus on clinical characteristics that cannot be linked to specific biological models. Here, we examined multimorbidities to identify MDD subtypes with distinct genetic and non-genetic factors. We leveraged dynamic Bayesian network approaches to determine a minimal set of multimorbidities relevant to MDD and identified seven clusters of disease-burden trajectories throughout the lifespan among 1.2 million participants from cohorts in the UK, Finland, and Spain. The clusters had clear protective- and risk-factor profiles as well as age-specific clinical courses mainly driven by inflammatory processes, and a comprehensive map of heritability and genetic correlations among these clusters was revealed. Our results can guide the development of personalized treatments for MDD based on the unique genetic, clinical and non-genetic risk-factor profiles of patients. Major depressive disorder is a heterogeneous condition with varied presentation of symptoms, comorbidities, and related genetic factors. This study aimed to identify clusters of major depressive disorder-related longitudinal multimorbidity trajectories and to characterize the clusters using genetic and risk factor data. The heterogeneity and complexity of symptom presentation, comorbidities and genetic factors pose challenges to the identification of biological mechanisms underlying complex diseases. Current approaches used to identify biological subtypes of major depressive disorder (MDD) mainly focus on clinical characteristics that cannot be linked to specific biological models. Here, we examined multimorbidities to identify MDD subtypes with distinct genetic and non-genetic factors. We leveraged dynamic Bayesian network approaches to determine a minimal set of multimorbidities relevant to MDD and identified seven clusters of disease-burden trajectories throughout the lifespan among 1.2 million participants from cohorts in the UK, Finland, and Spain. The clusters had clear protective- and risk-factor profiles as well as age-specific clinical courses mainly driven by inflammatory processes, and a comprehensive map of heritability and genetic correlations among these clusters was revealed. Our results can guide the development of personalized treatments for MDD based on the unique genetic, clinical and non-genetic risk-factor profiles of patients.Major depressive disorder is a heterogeneous condition with varied presentation of symptoms, comorbidities, and related genetic factors. This study aimed to identify clusters of major depressive disorder-related longitudinal multimorbidity trajectories and to characterize the clusters using genetic and risk factor data. Abstract The heterogeneity and complexity of symptom presentation, comorbidities and genetic factors pose challenges to the identification of biological mechanisms underlying complex diseases. Current approaches used to identify biological subtypes of major depressive disorder (MDD) mainly focus on clinical characteristics that cannot be linked to specific biological models. Here, we examined multimorbidities to identify MDD subtypes with distinct genetic and non-genetic factors. We leveraged dynamic Bayesian network approaches to determine a minimal set of multimorbidities relevant to MDD and identified seven clusters of disease-burden trajectories throughout the lifespan among 1.2 million participants from cohorts in the UK, Finland, and Spain. The clusters had clear protective- and risk-factor profiles as well as age-specific clinical courses mainly driven by inflammatory processes, and a comprehensive map of heritability and genetic correlations among these clusters was revealed. Our results can guide the development of personalized treatments for MDD based on the unique genetic, clinical and non-genetic risk-factor profiles of patients. The heterogeneity and complexity of symptom presentation, comorbidities and genetic factors pose challenges to the identification of biological mechanisms underlying complex diseases. Current approaches used to identify biological subtypes of major depressive disorder (MDD) mainly focus on clinical characteristics that cannot be linked to specific biological models. Here, we examined multimorbidities to identify MDD subtypes with distinct genetic and non-genetic factors. We leveraged dynamic Bayesian network approaches to determine a minimal set of multimorbidities relevant to MDD and identified seven clusters of disease-burden trajectories throughout the lifespan among 1.2 million participants from cohorts in the UK, Finland, and Spain. The clusters had clear protective- and risk-factor profiles as well as age-specific clinical courses mainly driven by inflammatory processes, and a comprehensive map of heritability and genetic correlations among these clusters was revealed. Our results can guide the development of personalized treatments for MDD based on the unique genetic, clinical and non-genetic risk-factor profiles of patients.The heterogeneity and complexity of symptom presentation, comorbidities and genetic factors pose challenges to the identification of biological mechanisms underlying complex diseases. Current approaches used to identify biological subtypes of major depressive disorder (MDD) mainly focus on clinical characteristics that cannot be linked to specific biological models. Here, we examined multimorbidities to identify MDD subtypes with distinct genetic and non-genetic factors. We leveraged dynamic Bayesian network approaches to determine a minimal set of multimorbidities relevant to MDD and identified seven clusters of disease-burden trajectories throughout the lifespan among 1.2 million participants from cohorts in the UK, Finland, and Spain. The clusters had clear protective- and risk-factor profiles as well as age-specific clinical courses mainly driven by inflammatory processes, and a comprehensive map of heritability and genetic correlations among these clusters was revealed. Our results can guide the development of personalized treatments for MDD based on the unique genetic, clinical and non-genetic risk-factor profiles of patients. |
| ArticleNumber | 7190 |
| Author | Kuokkanen, Mikko Van der Auwera, Sandra Garvert, Linda Cano, Isaac González-Colom, Rubèn Bolgar, Bence Eszlari, Nora Nagy, Tamas Schmidt, Carsten O. Millinghoffer, Andras Hullam, Gabor Bonk, Sarah Kirchner, Kevin Gezsi, Andras Mäkinen, Hannu Gal, Zsofia Paajanen, Teemu Roca, Josep Gonda, Xenia Juhasz, Gabriella Antal, Peter |
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Institut d’Investigacions Biomèdiques August Pi i Sunyer (FRCB-IDIBAPS), Universitat de Barcelona – sequence: 9 givenname: Xenia orcidid: 0000-0001-9015-4203 surname: Gonda fullname: Gonda, Xenia organization: Department of Pharmacodynamics, Faculty of Pharmaceutical Sciences, Semmelweis University, NAP3.0-SE Neuropsychopharmacology Research Group, Hungarian Brain Research Program, Semmelweis University, Department of Psychiatry and Psychotherapy, Semmelweis University – sequence: 10 givenname: Linda orcidid: 0000-0001-9067-8170 surname: Garvert fullname: Garvert, Linda organization: Department of Psychiatry and Psychotherapy, University Medicine Greifswald – sequence: 11 givenname: Teemu orcidid: 0000-0003-3499-4800 surname: Paajanen fullname: Paajanen, Teemu organization: Department of Public Health and Welfare, Population Health Unit, Public Health Research Team, Finnish Institute for Health and Welfare – sequence: 12 givenname: Zsofia orcidid: 0000-0002-9441-1497 surname: Gal fullname: Gal, Zsofia organization: Department of Pharmacodynamics, Faculty of Pharmaceutical Sciences, Semmelweis University, NAP3.0-SE Neuropsychopharmacology Research Group, Hungarian Brain Research Program, Semmelweis University – sequence: 13 givenname: Kevin surname: Kirchner fullname: Kirchner, Kevin organization: Department of Psychiatry and Psychotherapy, University Medicine Greifswald – sequence: 14 givenname: Andras surname: Millinghoffer fullname: Millinghoffer, Andras organization: Abiomics Europe Ltd – sequence: 15 givenname: Carsten O. orcidid: 0000-0001-5266-9396 surname: Schmidt fullname: Schmidt, Carsten O. organization: Institute for Community Medicine, University Medicine Greifswald – sequence: 16 givenname: Bence surname: Bolgar fullname: Bolgar, Bence organization: Department of Artificial Intelligence and Systems Engineering, Budapest University of Technology and Economics – sequence: 17 givenname: Josep surname: Roca fullname: Roca, Josep organization: Clínic Barcelona, Fundació de Recerca Clinic Barcelona - Institut d’Investigacions Biomèdiques August Pi i Sunyer (FRCB-IDIBAPS), Universitat de Barcelona – sequence: 18 givenname: Isaac orcidid: 0000-0003-2938-7459 surname: Cano fullname: Cano, Isaac organization: Clínic Barcelona, Fundació de Recerca Clinic Barcelona - Institut d’Investigacions Biomèdiques August Pi i Sunyer (FRCB-IDIBAPS), Universitat de Barcelona – sequence: 19 givenname: Mikko orcidid: 0000-0003-4375-9327 surname: Kuokkanen fullname: Kuokkanen, Mikko organization: Department of Public Health and Welfare, Population Health Unit, Public Health Research Team, Finnish Institute for Health and Welfare, Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine at University of Texas Rio Grande Valley, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki – sequence: 20 givenname: Peter orcidid: 0000-0002-4370-2198 surname: Antal fullname: Antal, Peter organization: Department of Artificial Intelligence and Systems Engineering, Budapest University of Technology and Economics – sequence: 21 givenname: Gabriella orcidid: 0000-0002-5975-4267 surname: Juhasz fullname: Juhasz, Gabriella email: juhasz.gabriella@semmelweis.hu organization: Department of Pharmacodynamics, Faculty of Pharmaceutical Sciences, Semmelweis University, NAP3.0-SE Neuropsychopharmacology Research Group, Hungarian Brain Research Program, Semmelweis University |
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| PublicationTitleAbbrev | Nat Commun |
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| PublicationYear | 2024 |
| Publisher | Nature Publishing Group UK Nature Publishing Group Nature Portfolio |
| Publisher_xml | – name: Nature Publishing Group UK – name: Nature Publishing Group – name: Nature Portfolio |
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