Evidence map›Paper›PMID 39542009›Full record

ArticleTuberculosis and respiratory diseases2025

Air Pollution and Interstitial Lung Disease.

Yong Suk Jo, Jin Woo Song

Abstract read
In one paragraph

Article in Tuberculosis and respiratory diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Yong Suk JoDivision of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
Jin Woo SongDepartment of Pulmonology and Critical Care Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.

Funding

Korea National Institute of Health 2024ER090500Ministry of Environment RS-2022-KE002197Ministry of Science and ICTNational Research Foundation of Korea 2022M3A9E4082647National Research Foundation of Korea 2022R1A2B 5B02001602
6 · The paper itself

Abstract

This review article explores the multifaceted relationship between air pollution and interstitial lung diseases (ILDs), particularly focusing on idiopathic pulmonary fibrosis, the most severe form of fibrotic ILD. Air pollutants are mainly composed of particulate matter, ozone (O3), nitrogen dioxide (NO2), carbon monoxide (CO), and sulfur dioxide (SO2). They are recognized as risk factors for several respiratory diseases. However, their specific effects on ILDs and related mechanisms have not been thoroughly studied yet. Emerging evidence suggests that air pollutants may contribute to the development and acute exacerbation of ILDs. Longitudinal studies have indicated that air pollution can adversely affect the prognosis of disease by decreasing lung function and increasing mortality. Lots of in vitro, in vivo , and epidemiologic studies have proposed possible mechanisms linking ILDs to air pollution, including inflammation and oxidative stress induced by exposure to air pollutants, which may induce mitochondrial dysfunction, promote cellular senescence, and disrupt normal epithelial repair processes. Despite these findings, effective interventions to mitigate effects of air pollution on ILD are not well established yet. This review emphasizes the urgent need to address air pollution as a key environmental risk factor for ILDs and calls for further studies to clarify its effects and develop preventive and therapeutic strategies.

Indexed as

Air PollutionInterstitial Lung DiseasePrognosis

Identifiers

PMID39542009
PMCPMC11704737

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.