Guo Hua, Zhao Xiaohan, Han Ke, Wang Yanqing
School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China; The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China.
Department of Rehabilitation, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.
Brain Res Bull. 2025 Jul;227:111402. doi: 10.1016/j.brainresbull.2025.111402. Epub 2025 May 21.
The mechanistic relationship between respiratory disorders and brain function remains poorly understood, despite growing evidence of cognitive and neurological manifestations in respiratory diseases. We aim to identify whether specific brain network connectivity patterns causally influence respiratory disease susceptibility, while respiratory conditions might reciprocally affect brain network architecture.
We performed bidirectional Mendelian randomization (MR) analyses using genome-wide association studies (GWAS) of brain network connectivity from UK Biobank resting-state functional MRI (rs-fMRI) data (N = 31,453) and GWAS data from ten major respiratory conditions: chronic obstructive pulmonary disease (COPD), asthma, idiopathic pulmonary fibrosis (IPF), sleep apnea syndrome (SAS), lung squamous carcinoma (LUSC), lung adenocarcinoma (LUAD), small cell lung carcinoma (SCLC), hospitalized COVID-19, very severe COVID-19, and bronchiectasis. Five MR methods, inverse variance weighted (IVW) with multiplicative random-effect model, weighted median, weighted mode, MR Egger, and MR-robust adjusted profile score (MR-RAPS) were employed to ensure causal inference.
In forward analysis, five respiratory disorders - asthma, IPF, SAS, LUSC, and very severe COVID-19 - showed significant causal associations (p < 1.31 ×10) with 11 rs-fMRI phenotypes, spanning multiple brain networks including the central executive, subcortical-cerebellum, motor, limbic, attention, salience, visual, and default mode networks. In reverse analysis, twelve brain functional networks demonstrated genetic associations with eight respiratory conditions (COPD, asthma, IPF, SAS, LUSC, SCLC, hospitalized COVID-19, and very severe COVID-19), predominantly involving attention, salience, default mode, visual, and central executive networks.
Our study provides preliminary genetic evidence suggesting potential causal relationships between brain network connectivity and respiratory disorders, contributing to our understanding of the lung-brain axis. While the identification of disease-specific network alterations offers promising insights, further clinical validation is needed before these findings can be translated into therapeutic interventions.
尽管越来越多的证据表明呼吸系统疾病存在认知和神经学表现，但呼吸障碍与脑功能之间的机制关系仍知之甚少。我们旨在确定特定的脑网络连接模式是否会因果性地影响呼吸系统疾病易感性，而呼吸状况可能会反过来影响脑网络结构。
我们使用来自英国生物银行静息态功能磁共振成像（rs-fMRI）数据（N = 31453）的脑网络连接全基因组关联研究（GWAS）以及来自十种主要呼吸疾病的GWAS数据进行双向孟德尔随机化（MR）分析：慢性阻塞性肺疾病（COPD）、哮喘、特发性肺纤维化（IPF）、睡眠呼吸暂停综合征（SAS）、肺鳞状细胞癌（LUSC）、肺腺癌（LUAD）、小细胞肺癌（SCLC）、住院的2019冠状病毒病（COVID-19）、非常严重的COVID-19和支气管扩张症。采用了五种MR方法，即具有乘性随机效应模型的逆方差加权（IVW）、加权中位数、加权模式、MR Egger和MR稳健调整轮廓评分（MR-RAPS）来确保因果推断。
在前瞻性分析中，五种呼吸系统疾病——哮喘、IPF、SAS、LUSC和非常严重的COVID-19——与十一种rs-fMRI表型显示出显著的因果关联（p < 1.31×10）, 这些表型跨越多个脑网络，包括中央执行网络、皮质下-小脑网络、运动网络、边缘系统网络、注意力网络、突显网络、视觉网络和默认模式网络。在反向分析中，十二个脑功能网络显示出与八种呼吸疾病（COPD、哮喘、IPF、SAS、LUSC、SCLC、住院的COVID-19和非常严重的COVID-19）的遗传关联，主要涉及注意力网络、突显网络、默认模式网络、视觉网络和中央执行网络。
我们的研究提供了初步的遗传证据，表明脑网络连接与呼吸障碍之间存在潜在的因果关系，有助于我们对肺-脑轴的理解。虽然识别疾病特异性的网络改变提供了有前景的见解，但在这些发现能够转化为治疗干预措施之前，还需要进一步的临床验证。
