Longitudinal profiling of upper respiratory tract microbiota and metabolome in hospitalized COVID-19 convalescents: a 3-year prospective cohort study.

BACKGROUND

Long COVID is characterized by persistent, far-reaching effects in convalescent individuals, with pulmonary diffusion impairment emerging as a clinically impactful sequela affecting more than one-third of this population. The salivary microbiome and metabolome, reflecting the oral-lung axis, offer a window into the mechanisms underlying this condition. However, systematic longitudinal evidence on their long-term dynamics after infection and their predictive value for persistent pulmonary diffusion impairment remains scarce.

METHODS

In this prospective cohort, we profiled the salivary bacterial microbiome (16S rRNA sequencing) and metabolome (untargeted LC-MS/MS) in 424 COVID-19 convalescents at 2 (T1) and 3 (T2) years post-discharge, alongside 106 demographically matched healthy controls. To explore whether 2-year salivary multiomics signatures were associated with 3-year pulmonary diffusion status, microbial and metabolic features were ranked using random forest mean decrease in accuracy and used to train 10 machine-learning classifiers after stratified training/internal validation splitting. Because this modeling strategy was exploratory, we further performed a repeated stability-selection analysis across 100 stratified resampling iterations to identify reproducibly selected salivary multiomics features.

RESULTS

COVID-19 convalescents exhibited sustained, interrelated salivary microbiome dysbiosis and metabolic reprogramming at 3 years post-infection. The microbial perturbations were characterized by reduced alpha diversity, a shift in phylogenetic dominance from Bacteroidota to Actinobacteriota, and a marked expansion of Proteobacteria at the 2-year follow-up. The microbial co-occurrence networks also became sparser, suggesting diminished stability. Metabolomic profiling revealed upregulation of the TCA cycle, purine/pyrimidine metabolism, arginine biosynthesis, and other pathways at the 2-year follow-up, with a discernible trend toward recovery by year 3. Notably, 36.6% of patients presented with persistent pulmonary diffusion dysfunction at the 3-year follow-up. Leveraging 2-year salivary multiomics signatures, we developed an exploratory proof-of-concept model for predicting 3-year pulmonary diffusion dysfunction. The CatBoost classifier achieved the best overall performance, achieving an area under the curve of 0.808 in the internal validation set; key predictive features included genera Catonella and Actinomyces, and metabolites adenosine 3',5'-diphosphate, triiodothyronine sulfate and betaine. In an exploratory stability-selected analysis, a conservatively tuned CatBoost model based on repeatedly selected features achieved an internal validation AUC of 0.798.

CONCLUSIONS

This research provides the first longitudinal characterization of the salivary bacterial microbiome and metabolome in COVID-19 convalescents up to 3 years post infection. Furthermore, we developed a novel predictive model for post-SARS-CoV-2 pulmonary diffusion impairment based on salivary multiomics features, which may represent a promising screening tool for identifying high-risk individuals.