Evidence map›Paper›PMID 41965787›Full record

ArticleClinical epigenetics2026

Lung microbiome predictors of epigenetic aging and potential associations with smoking and electronic cigarette use.

Ajmal Khan, Boseung Choi, Seungbae Kang, Daniel Y Weng, Kevin Ying, Joseph P McElroy, Sahar Kamel, Sarah A Reisinger, Mark D Wewers, Peter G Shields and 1 more

Abstract read
In one paragraph

Article in Clinical epigenetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Ajmal Khan *Division of Environmental Health Sciences, College of Public Health, The Ohio State University, Cunz Hall, 1841 Neil Ave., Columbus, OH, 43210, USA.
Boseung Choi *Biomedical Mathematics Group, Institute for Basic Science, Daejeon, Republic of Korea.
Seungbae KangDepartment of Big Data Science, Korea University, Sejong, Republic of Korea.
Daniel Y WengComprehensive Cancer Center, The Ohio State University and James Cancer Hospital, Columbus, OH, 43210, USA.
Kevin YingComprehensive Cancer Center, The Ohio State University and James Cancer Hospital, Columbus, OH, 43210, USA.
Joseph P McElroyCenter for Biostatistics, College of Medicine, The Ohio State University Wexner Medical Center, Columbus, OH, USA.
Sahar KamelComprehensive Cancer Center, The Ohio State University and James Cancer Hospital, Columbus, OH, 43210, USA.
Sarah A ReisingerComprehensive Cancer Center, The Ohio State University and James Cancer Hospital, Columbus, OH, 43210, USA.
Mark D WewersPulmonary and Critical Care Medicine, Department of Internal Medicine, The Ohio State University, Columbus, OH, USA.
Peter G ShieldsComprehensive Cancer Center, The Ohio State University and James Cancer Hospital, Columbus, OH, 43210, USA. Peter.Shields@osumc.edu.
Min-Ae SongDivision of Environmental Health Sciences, College of Public Health, The Ohio State University, Cunz Hall, 1841 Neil Ave., Columbus, OH, 43210, USA. Song.991@osu.edu.

Funding

Ministry of Education RS- 202300245056National Cancer Institute of the National Institutes of Health (NIH) (P30 CA016058)The Clinical and Translational Science Award (CTSA) (UM1TR004548)The Food and Drug Administration Center for Tobacco Products (CTP) (P50CA180908)The National Center for Advancing Translational Sciences (UL1TR001070)
6 · The paper itself

Abstract

backgroundThe lungs harbor diverse microbial communities that may influence pulmonary health, potentially through lung aging. While accelerated lung aging can increase susceptibility to pulmonary diseases, no studies have yet linked the lung microbiome to biological aging in disease-free individuals. MATERIALS AND

methodsWe assessed well-studied methylation-based biological aging (mAge) markers (Horvath, GrimAge, PhenoAge, and telomere-length) in the lungs of healthy smokers (SM), electronic cigarette (EC) users, and never-smokers (NS) (n = 26, 21-30 years). We used metatranscriptome profiling to detect live bacteria. Using XGBoost, we performed feature selection on 1016 bacterial species to predict faster or slower lung mAge, and the selected bacterial species were used as explanatory variables in a logistic regression model. Linear regression analyses examined the associations between identified bacterial species and urinary metabolites of exposure to smoking and EC use, including volatile organic compounds (VOCs) and polycyclic aromatic hydrocarbons (PAHs).

resultsThe logistic regression models identified bacterial species that classified individuals with faster or slower lung aging based on each mAge estimate (accuracy 77%-85%; AUC 0.78-0.91). Two species strongly predictive of GrimAge, Alistipes finegoldii and Arachidicoccus sp.BS20 were significantly less present in SM compared to NS. Arachidicoccus sp.BS20 was significantly associated with nicotine-intake-adjusted metabolites of several VOCs and PAHs in SM and EC users.

conclusionFor the first time, our study suggests potential associations of the microbiome with biological aging in the lungs of healthy individuals. In addition, the findings indicate that exposure to smoking and EC may be linked to shifts in particular microbial profiles associated with biological aging of the lungs. These results support the need for larger studies to better understand the direction and possible mechanisms of these relationships, and to further explore the lung microbiome as a potential target for interventions aimed at mitigating pulmonary aging and disease risk.

Indexed as

AgingLungMicrobiotaSmokingAdultDNA MethylationElectronic Nicotine Delivery SystemsEpigenesis, GeneticFemaleHumansMaleYoung AdultCigaretteElectronic cigaretteEpigenetic agingLung microbiomePAHsVOCs

Identifiers

PMID41965787
PMCPMC13214254

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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.