Department of Artificial Intelligence and Systems Engineering, Budapest University of Technology and Economics, Budapest, Hungary.
Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany.
Nat Commun. 2024 Aug 21;15(1):7190. doi: 10.1038/s41467-024-51467-7.
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.
症状表现、合并症和遗传因素的异质性和复杂性给复杂疾病的生物学机制的确定带来了挑战。目前用于识别重度抑郁症（MDD）生物学亚型的方法主要集中在无法与特定生物学模型相关联的临床特征上。在这里，我们检查了多种合并症，以确定具有不同遗传和非遗传因素的 MDD 亚型。我们利用动态贝叶斯网络方法确定了与 MDD 相关的最小一组多种合并症，并在英国、芬兰和西班牙的队列中 120 万名参与者中确定了一生中疾病负担轨迹的七个聚类。这些聚类具有明确的保护因素和风险因素特征，以及主要由炎症过程驱动的特定年龄的临床过程，并揭示了这些聚类之间遗传力和遗传相关性的综合图谱。我们的研究结果可以为基于患者独特的遗传、临床和非遗传风险因素特征为 MDD 制定个性化治疗方案提供指导。
