Oral microbiota for diagnosis of etomidate abuse via machine learning

Abstract

Aim: Illicit etomidate (ET) use in e-cigarettes has increased recently, but its effects on the oral microbiome remain unknown. This study investigates oral microbiota alterations in chronic ET users.

Methods: Saliva from 45 ET users and 44 controls underwent 16S ribosomal RNA (rRNA) sequencing. We compared microbial diversity, composition, predicted functions, and co-occurrence networks between groups, and developed 12 machine learning models to classify ET users based on microbial features.

Results: Chronic ET-containing e-cigarette use was strongly associated with significant oral microbial dysbiosis. Species richness and diversity were significantly lower in the ET group. ET users exhibited proliferation of taxa associated with oral diseases, including Actinomyces, Rothia, and Atopobium. PICRUSt2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States 2) predicted enhanced carbohydrate and amino acid metabolism in the ET group, while controls showed greater abundance of biotin and fatty acid metabolism pathways. Network analysis revealed reduced complexity and stability in the ET group. The ensemble model GLMBoost (generalized linear model boosting) + Random Forest achieved an area under the curve (AUC) of 1.00 and 100% accuracy on the test set. Seven key genera were identified as discriminative biomarkers: Prevotella_7, Rothia, Neisseria, Veillonella, Haemophilus, Actinomyces, and Fusobacterium.

Conclusion: Chronic use of ET-containing e-cigarettes is linked to altered homeostasis of the oral microbiota, providing a deeper understanding of how substance addiction impacts oral microbial ecology and highlights the potential of machine learning-derived microbial signatures as non-invasive tools for accurately distinguishing ET-containing e-cigarette users from non-users.

Keywords

Etomidate abuse / 16S rRNA sequencing / oral microbiota / microbial biomarkers / machine learning

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