IADR Abstract Archives
Correlation Between Clinical Variables and Subgingival Microbiota
Objectives: The use of high-throughput sequencing has offered scientists a more complex and realistic picture of the composition of the subgingival microbiota. Such advancements might push periodontal medicine to a more precise, personalized and predictive era. Merging periodontal clinical variables (PCV) with other demographic and systemic clinical variables might allow to the development of predictive tools of the microbiome composition. The aim of this study was to study the correlation between the PCV and the microbial composition obtained by high-throughput sequencing.
Methods: Subgingival samples were taken from periodontally healthy subjects (HS, n = 56) and periodontitis patients (PP, n = 121). Age, gender, bleeding on probing, clinical attachment loss and periodontal probing depth were recorded by the clinicians. Microbial composition was assessed through high-throughput sequencing of the V3-V4 regions of the 16S rRNA gene. Sequence analysis was performed following the DADA2 pipeline, and alpha and beta diversity were explored with the phyloseq R package. Associations between the studied variables were analysed with non-parametrical statistical tests, lineal models and correlation tests.
Results: Significant differences were observed regarding the PCV of the samples between HS and PP. PCV showed little impact regarding the alpha diversity of the subgingival microbiota, except for the gender in HS, which showed higher richness (p = 0.04) and diversity (p = 0.043) in females. On the other hand, the gender in PP (p = 0.047) and the diagnose (p = 0.001) were able to significantly explain differences in the beta diversity. Significant differences were also observed regarding the differential abundance of certain genera.
Conclusions: This study suggests that the association of PCV with the composition of the subgingival microbiota is not strong enough to allow the creation of predictive models. This model might be accomplished by broadening the array of variables and using identification techniques with higher resolution.
Methods: Subgingival samples were taken from periodontally healthy subjects (HS, n = 56) and periodontitis patients (PP, n = 121). Age, gender, bleeding on probing, clinical attachment loss and periodontal probing depth were recorded by the clinicians. Microbial composition was assessed through high-throughput sequencing of the V3-V4 regions of the 16S rRNA gene. Sequence analysis was performed following the DADA2 pipeline, and alpha and beta diversity were explored with the phyloseq R package. Associations between the studied variables were analysed with non-parametrical statistical tests, lineal models and correlation tests.
Results: Significant differences were observed regarding the PCV of the samples between HS and PP. PCV showed little impact regarding the alpha diversity of the subgingival microbiota, except for the gender in HS, which showed higher richness (p = 0.04) and diversity (p = 0.043) in females. On the other hand, the gender in PP (p = 0.047) and the diagnose (p = 0.001) were able to significantly explain differences in the beta diversity. Significant differences were also observed regarding the differential abundance of certain genera.
Conclusions: This study suggests that the association of PCV with the composition of the subgingival microbiota is not strong enough to allow the creation of predictive models. This model might be accomplished by broadening the array of variables and using identification techniques with higher resolution.