Genetic influence of the brain imaging phenotypes, brain and cerebrospinal fluid metabolites and brain genes on migraine subtypes: a Mendelian randomization and multi-omics study.
作者信息
Zhang Ping-An, Wang Jie-Lin, Dong Mei-Hua, Huang Xiang-Chun, Li Nai-Jian, Qin Run-Dong, Li Jing
机构信息
Department of Allergy and Clinical Immunology, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, No.151, Yanjiangxi Road, Yuexiu District, Guangzhou, Guangdong, 510120, China.
Department of Obstetrics and Gynecology, Department of Gynecologic Oncology Research Office, Guangzhou Key Laboratory of Targeted Therapy for Gynecologic Oncology, Guangdong Provincial Key Laboratory of Major Obstetric Diseases, Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology, Guangdong-Hong Kong-Macao Greater Bay Area Higher Education Joint Laboratory of Maternal-Fetal Medicine, The Third Affiliated Hospital, Guangzhou Medical University, Guangzhou, China.
出版信息
J Headache Pain. 2025 May 20;26(1):124. doi: 10.1186/s10194-025-02063-7.
BACKGROUND
Migraine is a complex neurological disorder with high prevalence but unclear pathogenesis. Numerous studies have suggested that migraine is associated with alterations in brain imaging phenotypes (BIPs) and dysregulation of cerebrospinal fluid (CSF) and brain metabolism; however, causal evidence remains limited. Mendelian randomization (MR) offers a powerful approach for inferring causality using genetic instruments.
METHODS
Firstly, we conducted linkage disequilibrium score regression (LDSC) to evaluate genetic correlations between migraine, including the migraine with aura (MA) and migraine without aura (MO) subtypes, and BIPs, CSF, and brain metabolites. Traits that showed genetic correlations with migraine, MA, or MO were retained for subsequent MR analysis with the corresponding migraine phenotype. Traits showing significant correlations were analyzed using bidirectional two-sample MR (TSMR), followed by two-step TSMR to identify cross-omics mediation effects. Additionally, We also applied summary-data-based MR (SMR) to detect brain-region-specific genes with potential causal effects. Enrichment analyses (KEGG, GO, PPI, transcription factor, and miRNA networks) were conducted to further explore underlying mechanisms.
RESULTS
LDSC identified significant genetic correlations with 73 BIPs and 40 metabolites for overall migraine, 71 BIPs and 37 metabolites for MA, and 49 BIPs and 62 metabolites for MO. Enrichment analysis revealed that genetically associated metabolites were predominantly involved in amino acid metabolic pathways. TSMR identified 6 BIPs and 2 metabolites causally linked to overall migraine, 3 BIPs and 3 metabolites to MA, and 2 BIPs and 5 metabolites to MO. Most migraine-related BIPs mapped to the parietal lobe. Reverse MR analysis showed that overall migraine causally influenced 4 BIPs and 3 metabolites, while MA and MO affected 1 BIP and 1 metabolite, and 3 BIPs and 1 metabolite, respectively. Mediation analysis revealed five significant mediation pathways were identified. SMR analysis identified FAM83B and CIB2 consistently showing inhibitory effects across most regions. Enrichment analysis showed that these genes were predominantly involved in immune activation and cell adhesion.
CONCLUSIONS
Our study integrates cross-omics analyses to investigate the causal links between brain structure, metabolic alterations, gene expression, and migraine including its MA and MO subtypes. These findings provide novel insights into the pathophysiological mechanisms and potential targets for intervention across migraine subtypes.
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