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The study investigates the relationship between lung microbiome characteristics and biological aging in healthy individuals, focusing on smokers and electronic cigarette users.

April 13, 2026Clinical Epigenetics0 citationsOpen Access

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

AKA.R. KhanOhio Department of Health BCBoseung ChoiKorea University SKSeungbae KangKorea University

Key Points

The study investigates the relationship between lung microbiome characteristics and biological aging in healthy individuals, focusing on smokers and electronic cigarette users.
Assessed biological aging markers through methylation analyses including Horvath, GrimAge, PhenoAge, and telomere-length.
Utilized metatranscriptome profiling to identify live bacterial communities in the lungs.
Employed XGBoost for feature selection on 1016 bacterial species to predict lung aging.
Conducted logistic regression analysis using selected bacterial species to classify lung aging.
Performed linear regression analyses to explore associations with urinary metabolites from smoking and e-cigarette exposure.
Logistic regression models achieved 77%-85% accuracy and AUC scores of 0.78-0.91 in classifying participants by lung aging.
Two bacterial species, Alistipes finegoldii and Arachidicoccus sp.BS20, were significantly less present in smokers compared to never-smokers.
Arachidicoccus sp.BS20 showed associations with specific metabolites related to smoking and e-cigarette exposure.

Abstract

The 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. We 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). The 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. For 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.

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