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影响慢性阻塞性肺疾病患病率和死亡率的因素:一项涵盖1990年至2021年201个国家和地区的全数据库生态研究。

Factors influencing the prevalence and death rate of COPD: a pan-database ecological study covering 201 countries and regions from 1990 to 2021.

作者信息

Wang Zihui, Cao Wenhan, You Zhixuan, Li Shaoqiang, Xue Mingshan, Li Haiyang, Lin Junfeng, Cai Guannan, Chen Yuqi, Liang Zhiman, Zhou Chengtao, Wu Xiaofang, Dong Guanghui, Zhong Nanshan, Sun Baoqing, Cheng Zhangkai J

机构信息

State Key Laboratory of Respiratory Disease, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou Laboratory, China.

Department of Clinical Laboratory, Dermatology Hospital, Southern Medical University, Guangzhou, China.

出版信息

EClinicalMedicine. 2025 Aug 1;86:103347. doi: 10.1016/j.eclinm.2025.103347. eCollection 2025 Aug.

DOI:10.1016/j.eclinm.2025.103347
PMID:40791891
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12337789/
Abstract

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a common, heterogeneous disease and may be influenced by diverse factors. However, gaps remain in previous studies regarding the exploration of potential influencing factors. This study aims to investigate the wide range of potential factors influencing COPD based on a pan-database ecological analysis.

METHODS

We integrated data from 17 global databases (e.g., the Global Burden of Disease Study) which encompass social, environmental, and health data across various regions. Generalized linear regression was used to analyze the association of cumulative and instant exposures of factors with COPD and to rank their importance. Spearman analysis was used to assess the correlation of various factors with COPD. Heatmaps, scatter plots, and nonclassical multidimensional scaling (i.e., network graph) were employed to visualize the correlations.

FINDINGS

The study aggregated 77 social and environmental factors, 85 lifestyle and dietary factors, 25 physiological indicators, and 28 diseases. In the cumulative exposure analysis, tobacco consumption, atmospheric pollutants (e.g., ozone, CO, and organic matter aerosol), biomass cooking, and climatic conditions (e.g., vapor pressure, solar radiation, and temperature) were found to be associated with COPD prevalence. Additionally, tobacco consumption, social factors (e.g., hunger index, gender inequality index, and education year), and climatic conditions significantly impacted death rates. The results for cumulative exposures were consistent with those for instant exposures. Network graph analysis indicated a positive correlation between COPD and chronic kidney disease, other chronic respiratory diseases, gout, and stroke.

INTERPRETATION

Various factors (e.g., tobacco consumption, atmospheric pollution, biomass cooking, temperature, social factors, and comorbidities) significantly influence COPD. Comprehensive interventions are needed to reduce the disease burden of COPD.

FUNDING

The Foundation of Guangzhou National Laboratory (SRPG22-018, SRPG22-016), State Key Laboratory of Respiratory Disease (SKLRD-OP-202402), Zhejiang medical health science and technology project (2025KY1245), Multi-Center Clinical Research Project of Guangzhou Medical University (GMUCR2025-02009), and Guangzhou Municipal Science and Technology Bureau (SL2024A04J00706).

摘要

背景

慢性阻塞性肺疾病(COPD)是一种常见的异质性疾病,可能受多种因素影响。然而,以往关于潜在影响因素探索的研究仍存在空白。本研究旨在基于全数据库生态分析,调查影响COPD的广泛潜在因素。

方法

我们整合了来自17个全球数据库(如全球疾病负担研究)的数据,这些数据库涵盖了不同地区的社会、环境和健康数据。采用广义线性回归分析因素的累积暴露和即时暴露与COPD的关联,并对其重要性进行排序。使用Spearman分析评估各种因素与COPD的相关性。采用热图、散点图和非经典多维缩放(即网络图)来可视化相关性。

结果

该研究汇总了77个社会和环境因素、85个生活方式和饮食因素、25个生理指标以及28种疾病。在累积暴露分析中,发现烟草消费、大气污染物(如臭氧、一氧化碳和有机气溶胶)、生物质烹饪以及气候条件(如蒸气压、太阳辐射和温度)与COPD患病率相关。此外,烟草消费、社会因素(如饥饿指数、性别不平等指数和受教育年限)以及气候条件对死亡率有显著影响。累积暴露的结果与即时暴露的结果一致。网络图分析表明COPD与慢性肾病、其他慢性呼吸道疾病、痛风和中风之间存在正相关。

解读

多种因素(如烟草消费、大气污染、生物质烹饪、温度、社会因素和合并症)对COPD有显著影响。需要采取综合干预措施来减轻COPD的疾病负担。

资助

广州实验室基金(SRPG22 - 018,SRPG22 - 016)、呼吸疾病国家重点实验室(SKLRD - OP - 202402)、浙江省医药卫生科技项目(2025KY1245)、广州医科大学多中心临床研究项目(GMUCR2025 - 02009)以及广州市科学技术局(SL2024A04J00706)。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a12/12337789/6e89a7d173c8/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a12/12337789/4d7707511942/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a12/12337789/348366643498/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a12/12337789/2ba7d8932173/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a12/12337789/5318e853b93c/gr4ab.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a12/12337789/6e89a7d173c8/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a12/12337789/4d7707511942/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a12/12337789/348366643498/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a12/12337789/2ba7d8932173/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a12/12337789/5318e853b93c/gr4ab.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a12/12337789/6e89a7d173c8/gr5.jpg

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