绘制肺脑轴：脑网络连接与呼吸系统疾病之间的因果关系。

IF 3.7 3区医学 Q2 NEUROSCIENCES

Brain Research Bulletin Pub Date : 2025-05-21 DOI:10.1016/j.brainresbull.2025.111402

Hua Guo , Xiaohan Zhao , Ke Han , Yanqing Wang

{"title":"绘制肺脑轴：脑网络连接与呼吸系统疾病之间的因果关系。","authors":"Hua Guo , Xiaohan Zhao , Ke Han , Yanqing Wang","doi":"10.1016/j.brainresbull.2025.111402","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>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.</div></div><div><h3>Methods</h3><div>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.</div></div><div><h3>Results</h3><div>In forward analysis, five respiratory disorders - asthma, IPF, SAS, LUSC, and very severe COVID-19 - showed significant causal associations (<em>p</em> < 1.31 ×10<sup>−4</sup>) 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.</div></div><div><h3>Conclusions</h3><div>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.</div></div>","PeriodicalId":9302,"journal":{"name":"Brain Research Bulletin","volume":"227 ","pages":"Article 111402"},"PeriodicalIF":3.7000,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Mapping the lung-brain axis: Causal relationships between brain network connectivity and respiratory disorders\",\"authors\":\"Hua Guo , Xiaohan Zhao , Ke Han , Yanqing Wang\",\"doi\":\"10.1016/j.brainresbull.2025.111402\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><div>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.</div></div><div><h3>Methods</h3><div>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.</div></div><div><h3>Results</h3><div>In forward analysis, five respiratory disorders - asthma, IPF, SAS, LUSC, and very severe COVID-19 - showed significant causal associations (<em>p</em> < 1.31 ×10<sup>−4</sup>) 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.</div></div><div><h3>Conclusions</h3><div>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.</div></div>\",\"PeriodicalId\":9302,\"journal\":{\"name\":\"Brain Research Bulletin\",\"volume\":\"227 \",\"pages\":\"Article 111402\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2025-05-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Brain Research Bulletin\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S036192302500214X\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"NEUROSCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Brain Research Bulletin","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S036192302500214X","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"NEUROSCIENCES","Score":null,"Total":0}

引用次数: 0

摘要

背景：呼吸系统疾病与脑功能之间的机制关系仍然知之甚少，尽管越来越多的证据表明呼吸系统疾病有认知和神经系统表现。我们的目标是确定特定的大脑网络连接模式是否会影响呼吸系统疾病的易感性，而呼吸系统疾病可能会相互影响大脑网络结构。方法：我们使用来自UK Biobank静息状态功能MRI数据（N=31,453）的脑网络连接的全基因组关联研究（GWAS）和来自10种主要呼吸疾病的GWAS数据进行双向孟德尔随机化分析：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）、肺鳞状癌（LUSC）、肺腺癌（LUAD）、小细胞肺癌（SCLC）、住院的COVID-19、非常严重的COVID-19、支气管扩张。采用五种MR方法，即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稳健调整的特征评分（MR- raps）来确保因果推理。结果：在正向分析中，五种呼吸系统疾病——哮喘、IPF、SAS、LUSC和非常严重的COVID-19——与11种rs-fMRI表型显示出显著的因果关系（p-4），跨越多个大脑网络，包括中央执行、皮质下小脑、运动、边缘、注意力、显著性、视觉和默认模式网络。在反向分析中，12个脑功能网络显示出与8种呼吸系统疾病（COPD、哮喘、IPF、SAS、LUSC、SCLC、住院COVID-19和非常严重的COVID-19）的遗传关联，主要涉及注意力、显著性、默认模式、视觉和中央执行网络。结论：我们的研究提供了初步的遗传证据，表明大脑网络连接与呼吸系统疾病之间存在潜在的因果关系，有助于我们对肺-脑轴的理解。虽然疾病特异性网络改变的识别提供了有希望的见解，但在将这些发现转化为治疗干预措施之前，还需要进一步的临床验证。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Mapping the lung-brain axis: Causal relationships between brain network connectivity and respiratory disorders

Background

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.

Methods

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.

Results

In forward analysis, five respiratory disorders - asthma, IPF, SAS, LUSC, and very severe COVID-19 - showed significant causal associations (p < 1.31 ×10⁻⁴) 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.

Conclusions

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.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Brain Research Bulletin 医学-神经科学

CiteScore

6.90

自引率

2.60%

发文量

253

审稿时长

67 days

期刊介绍： The Brain Research Bulletin (BRB) aims to publish novel work that advances our knowledge of molecular and cellular mechanisms that underlie neural network properties associated with behavior, cognition and other brain functions during neurodevelopment and in the adult. Although clinical research is out of the Journal''s scope, the BRB also aims to publish translation research that provides insight into biological mechanisms and processes associated with neurodegeneration mechanisms, neurological diseases and neuropsychiatric disorders. The Journal is especially interested in research using novel methodologies, such as optogenetics, multielectrode array recordings and life imaging in wild-type and genetically-modified animal models, with the goal to advance our understanding of how neurons, glia and networks function in vivo.