Evidence map›Paper›PMID 40224000›Full record

ArticleJournal of thoracic disease2025

Krebs von den Lungen-6 as a biomarker for distinguishing between interstitial lung disease and interstitial lung abnormalities based on computed tomography findings.

Jin Hwa Song, Hye-Rin Kang, Keun-Beom Cheong

Abstract read
In one paragraph

Article in Journal of thoracic disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

3 authors.

Jin Hwa SongDivision of Pulmonary, Allergy, and Critical Care Medicine, Department of Internal Medicine, Veterans Health Service Medical Center, Seoul, Korea.ORCID https://orcid.org/0000-0002-8161-3970
Hye-Rin KangDivision of Pulmonary, Allergy, and Critical Care Medicine, Department of Internal Medicine, Veterans Health Service Medical Center, Seoul, Korea.ORCID https://orcid.org/0000-0002-8852-8736
Keun-Beom CheongDivision of Pulmonary, Allergy, and Critical Care Medicine, Department of Internal Medicine, Veterans Health Service Medical Center, Seoul, Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Krebs von den Lungen-6 (KL-6), predominantly expressed by type II alveolar cells, is linked to the prognosis and severity of interstitial lung disease (ILD). This investigation sought to explore the relationship between KL-6 concentrations and the presence of ILD versus interstitial lung abnormalities (ILA). Methods: This single-center retrospective study included 147 patients who were suspected of ILD and underwent KL-6 testing between October 2022 and March 2023. Epidemiological and clinical details of the patients, including the outcomes of pulmonary function tests and computed tomography findings, were retrospectively extracted from electronic medical records. Results: The average age within the cohort was 77.1 years, with 97.3% (143 patients) being male. Notably, the ILA group displayed significantly reduced levels of KL-6 compared to the ILD group (411.6±298.8 Conclusions: Our findings indicate that serum KL-6 concentrations are markedly elevated in patients with clinically recognized and treated ILD compared to those identified with ILA. Furthermore, the association between higher KL-6 levels and reduced forced vital capacity percentage underlines the biomarker's potential in differentiating between ILD requiring intervention and less severe abnormalities.

Indexed as

Biomarkershumaninterstitiallung diseasesMUC1 protein

Identifiers

PMID40224000
PMCPMC11986748

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.