Graduation Year
2024
Document Type
Dissertation
Degree
Ph.D.
Degree Name
Doctor of Philosophy (Ph.D.)
Degree Granting Department
Public Health
Major Professor
Amy C. Alman, Ph.D.
Committee Member
Rays H.Y. Jiang, Ph.D.
Committee Member
Jason L. Salemi, Ph.D.
Committee Member
Anujit Sarkar, Ph.D.
Keywords
Bacteria, Glycemic control, Machine learning, Predictive models
Abstract
This study explores the associations between oral microbiomes, periodontal disease (PD), and systemic metabolism in individuals with and without Type 1 diabetes (T1D). Data were obtained from 208 participants (99 with T1D, 109 non-diabetic) in the Coronary Artery Calcification in Type 1 Diabetes (CACTI) pilot study and 283 participants from the Oral Microbiome ancillary cohort study. Both studies included the collection of saliva or subgingival plaque samples. Microbial DNA was extracted, and the V4 region of the 16S rRNA gene was sequenced using the Illumina MiSeq platform. Machine learning models, including Random Forest (RF) and Multi-layer Perceptron (MLP), were applied to classify PD severity and predict inflammation. Subgingival microbiome-based models effectively classified PD, with HbA1c levels contributing to model performance (F1 = 0.93). Significant correlations were identified between specific taxa, such as Fusobacterium and Treponema, and moderate/severe PD, particularly in T1D subjects. Distinct profiles between subgingival and salivary microbiomes were observed, with subgingival microbiomes demonstrating higher richness and average abundance in certain phyla. An RF model trained on subgingival microbiomes explained 50% of the variance in salivary TNF-alpha levels, outperforming models based on salivary microbiomes. Key microbial genera and dietary factors were important predictors of inflammation. Inflammation scores, based on salivary cytokine levels, were positively associated with systolic blood pressure and higher oral cytokine levels in T1D patients, while non-diabetic subjects did not show significant associations. The findings underscore the distinct roles of subgingival and salivary microbiomes in PD and inflammation, and highlight the potential of microbiome-based biomarkers for predicting disease and systemic health outcomes.
Scholar Commons Citation
Xiao, Mianli, "The Association between Oral Microbiome, Oral Inflammation, Periodontal Disease, and Systemic Metabolism in Individuals With/Without Type 1 Diabetes" (2024). USF Tampa Graduate Theses and Dissertations.
https://digitalcommons.usf.edu/etd/11213
