Association of general health and lifestyle factors with the salivary microbiota – Lessons learned from the ADDITION-PRO cohort

INTRODUCTION: Previous research indicates that the salivary microbiota may be a biomarker of oral as well as systemic disease. However, clarifying the potential bias from general health status and lifestyle-associated factors is a prerequisite of using the salivary microbiota for screening. MATERIALS & METHODS: ADDDITION-PRO is a nationwide Danish cohort, nested within the Danish arm of the Anglo-Danish-Dutch Study of Intensive treatment in People with Screen-Detected Diabetes in Primary Care. Saliva samples from n=746 individuals from the ADDITION-PRO cohort were characterized using 16s rRNA sequencing. Alpha- and beta diversity as well as relative abundance of genera was examined in relation to general health and lifestyle-associated variables. Permutational multivariate analysis of variance (PERMANOVA) was performed on individual variables and all variables together. Classification models were created using sparse partial-least squares discriminant analysis (sPLSDA) for variables that showed statistically significant differences based on PERMANOVA analysis (p < 0.05). RESULTS: Glycemic status, hemoglobin-A(1c) (HbA(1c)) level, sex, smoking and weekly alcohol intake were found to be significantly associated with salivary microbial composition (individual variables PERMANOVA, p < 0.05). Collectively, these variables were associated with approximately 5.8% of the observed differences in the composition of the salivary microbiota. Smoking status was associated with 3.3% of observed difference, and smoking could be detected with good accuracy based on salivary microbial composition (AUC 0.95, correct classification rate 79.6%). CONCLUSIONS: Glycemic status, HbA(1c) level, sex, smoking and weekly alcohol intake were significantly associated with the composition of the salivary microbiota. Despite smoking only being associated with 3.3% of the difference in overall salivary microbial composition, it was possible to create a model for detection of smoking status with a high correct classification rate. However, the lack of information on the oral health status of participants serves as a limitation in the present study. Further studies in other cohorts are needed to validate the external validity of these findings.