Guo Xiaonan, Feng Yu, Ji Xiaolong, Jia Ningning, Maimaiti Aierpati, Lai Jianbo, Wang Zheng, Yang Sheng, Hu Shaohua
Department of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Department of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Department of Psychiatry, Melbourne Neuropsychiatry Centre, The University of Melbourne, Carlton South, VIC, Australia.
EBioMedicine. 2025 Jan;111:105530. doi: 10.1016/j.ebiom.2024.105530. Epub 2024 Dec 27.
Increasing evidence suggests a complex interplay between psychiatric disorders and metabolic dysregulations. However, most research has been limited to specific disorder pairs, leaving a significant gap in our understanding of the broader psycho-metabolic nexus.
This study leveraged large-scale cohort data and genome-wide association study (GWAS) summary statistics, covering 8 common psychiatric disorders and 43 metabolic traits. We introduced a comprehensive analytical strategy to identify shared genetic bases sequentially, from key genetic correlation regions to local pleiotropy and pleiotropic genes. Finally, we developed polygenic risk score (PRS) models to translate these findings into clinical applications.
We identified significant bidirectional clinical risks between psychiatric disorders and metabolic dysregulations among 310,848 participants from the UK Biobank. Genetic correlation analysis confirmed 104 robust trait pairs, revealing 1088 key genomic regions, including critical hotspots such as chr3: 47588462-50387742. Cross-trait meta-analysis uncovered 388 pleiotropic single nucleotide variants (SNVs) and 126 shared causal variants. Among variants, 45 novel SNVs were associated with psychiatric disorders and 75 novel SNVs were associated with metabolic traits, shedding light on new targets to unravel the mechanism of comorbidity. Notably, RBM6, a gene involved in alternative splicing and cellular stress response regulation, emerged as a key pleiotropic gene. When psychiatric and metabolic genetic information were integrated, PRS models demonstrated enhanced predictive power.
The study highlights the intertwined genetic and clinical relationships between psychiatric disorders and metabolic dysregulations, emphasising the need for integrated approaches in diagnosis and treatment.
The National Key Research and Development Program of China (2023YFC2506200, SHH). The National Natural Science Foundation of China (82273741, SY).
越来越多的证据表明精神疾病与代谢失调之间存在复杂的相互作用。然而，大多数研究仅限于特定的疾病对，在我们对更广泛的心理-代谢关系的理解上留下了重大空白。
本研究利用大规模队列数据和全基因组关联研究（GWAS）汇总统计，涵盖8种常见精神疾病和43种代谢特征。我们引入了一种全面的分析策略，从关键遗传相关区域到局部多效性和多效性基因，依次识别共享的遗传基础。最后，我们开发了多基因风险评分（PRS）模型，将这些发现转化为临床应用。
在来自英国生物银行的310,848名参与者中，我们确定了精神疾病与代谢失调之间存在显著的双向临床风险。遗传相关性分析确认了104对稳健的性状对，揭示了1088个关键基因组区域，包括关键热点区域，如chr3: 47588462-50387742。跨性状荟萃分析发现了388个多效性单核苷酸变异（SNV）和126个共享因果变异。在这些变异中，45个新的SNV与精神疾病相关，75个新的SNV与代谢特征相关，为揭示共病机制的新靶点提供了线索。值得注意的是，参与可变剪接和细胞应激反应调节的基因RBM6成为关键的多效性基因。当整合精神和代谢遗传信息时，PRS模型显示出增强的预测能力。
该研究突出了精神疾病与代谢失调之间相互交织的遗传和临床关系，强调了在诊断和治疗中采用综合方法的必要性。
中国国家重点研发计划（2023YFC2506200，SHH）。中国国家自然科学基金（82273741，SY）。
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