Sputum production and salivary microbiome in COVID-19 patients reveals oral-lung axis.

SARS-CoV-2, a severe respiratory disease primarily targeting the lungs, was the leading cause of death worldwide during the pandemic. Understanding the interplay between the oral microbiome and inflammatory cytokines during acute infection is crucial for elucidating host immune responses. This study aimed to explore the relationship between the oral microbiome and cytokines in COVID-19 patients, particularly those with and without sputum production. Saliva and blood samples from 50 COVID-19 patients were subjected to 16S ribosomal RNA gene sequencing for oral microbiome analysis, and 65 saliva and serum cytokines were assessed using Luminex multiplex analysis. The Mann-Whitney test was used to compare cytokine levels between individuals with and without sputum production. Logistic regression machine learning models were employed to evaluate the predictive capability of oral microbiome, salivary, and blood biomarkers for sputum production. Significant differences were observed in the membership (Jaccard dissimilarity: p = 0.016) and abundance (PhILR dissimilarity: p = 0.048; metagenomeSeq) of salivary microbial communities between patients with and without sputum production. Seven bacterial genera, including Prevotella, Streptococcus, Actinomyces, Atopobium, Filifactor, Leptotrichia, and Selenomonas, were more prevalent in patients with sputum production (p<0.05, Fisher's exact test). Nine genera, including Prevotella, Megasphaera, Stomatobaculum, Selenomonas, Leptotrichia, Veillonella, Actinomyces, Atopobium, and Corynebacteria, were significantly more abundant in the sputum-producing group, while Lachnoanaerobaculum was more prevalent in the non-sputum-producing group (p<0.05, ANCOM-BC). Positive correlations were found between salivary IFN-gamma and Eotaxin2/CCL24 with sputum production, while negative correlations were noted with serum MCP3/CCL7, MIG/CXCL9, IL1 beta, and SCF (p<0.05, Mann-Whitney test). The machine learning model using only oral bacteria input outperformed the model that included all data: blood and saliva biomarkers, as well as clinical and demographic variables, in predicting sputum production in COVID-19 subjects. The performance metrics were as follows, comparing the model with only bacteria input versus the model with all input variables: precision (95% vs. 75%), recall (100% vs. 50%), F1-score (98% vs. 60%), and accuracy (82% vs. 66%).

Association between Frequency of Toothbrushing and Metabolic Syndrome among Adolescents: A 5-Year Follow-Up Study.

This study longitudinally examines the relationship between the frequency of toothbrushing and the development of selected components of metabolic syndrome (MetS), along with the potential role of salivary biomarkers in this relationship. In 2014, 6317 12-year-old children underwent health examinations (T1), of which, 348 children participated in the second stage of data collection in 2019 (T2). The association between the change in the metabolic status during the 5-year follow-up examination (between T1 and T2) and frequency of toothbrushing was assessed using multinomial logistic regression analyses. At T2, healthy adolescents had significantly higher odds of toothbrushing twice or more daily compared with adolescents with components of MetS (OR = 1.99, 95% CI 1.15-3.45). Adolescents who were healthy at T1 but developed components of MetS at T2, had significantly higher frequencies of dining-out compared with adolescents with components of MetS at both T1 and T2 (OR = 0.09, 95% CI 0.02 to 0.49). Adolescents who were 'healthy' at both T1 and T2 had significantly (p < 0.05) lower levels of C-reactive protein (T2), insulin (T1 and T2), interleukin-6 (T1) and adiponectin (T1) compared with adolescents who had components of MetS. Toothbrushing and frequency of dining-out were associated with the presence of MetS components.