Gum disease is one of the leading causes of tooth loss and affects nearly 50% of the global population. In addition to causing oral decay, gum disease also increases the risk for cardiometabolic disease. How could diseases of the oral cavity and cardiovascular system be related? One aspect that both diseases have in common is dysbiosis of the microbial community: severe periodontitis has been clearly associated with a distinct subset of pathogenic bacteria that colonize deep inside the periodontal pocket and exacerbate disease. More recently cardiometabolic disorders have been associated with microbial dysbiosis in the gut and oral cavity. However, in both cases it remains unclear whether these microbial changes are the cause or symptom of the disease.
To further explore the interplay of oral and cardiometabolic diseases with the oral microbiome, a diverse team of scientists led by Dr. Ryan Demmer created the Oral Infections Glucose Intolerance and Insulin Resistance Study (ORIGINS). This study collected subgingival plaque, blood, and detailed periodontal and cardiometabolic data from a group of hundreds of healthy volunteers in New York City. In this analysis, we performed 16S rRNA gene amplicon sequencing on >1,000 ORIGINS samples collected from 787 volunteers to assess the relative abundance of the thousands of microbial taxa inhabiting the subgingival pockets in both healthy and diseased sites.
As a graduate student in Dr. Rob Knight’s lab, I was excited to get involved with the analysis of this large and meticulously organized dataset, particularly because I had recently been involved with the development of new computational tools designed to handle the highly complex, sparse, and relative nature of the data generated from next-generation sequencing experiments. One of these tools, robust Aitchison PCA, was developed by a fellow graduate student, Cameron Martino [Martino 2019]. He borrowed algorithms originally designed to win the Netflix Prize [Bennett 2007] to deal with the sparsity and high dimensionality of amplicon sequencing datasets for principal components analysis. With this tool we observed a significant separation of healthy and diseased plaque samples along the largest axis of variation. The other timely tool, Songbird, was developed by another fellow graduate student, Jamie Morton [Morton 2019]. Jamie’s previous research had demonstrated the pitfalls of using amplicon sequence data to assign absolute differential abundances, and he worked to develop a framework to assess changes in microbial composition while accounting for compositionality. I had recently learned how to employ flow cytometry to determine absolute microbial load, and we used this technique to validate the songbird algorithm. This was the tool we used to determine which microbes are associated with gum disease, and then whether those microbial markers of early gum disease were also correlated with cardiometabolic outcomes.
Most of our samples (71%) came from healthy or mildly diseased subgingival pockets, yet even in this pre-pathological state we were able to identify lineages of microbes that were strongly correlated with periodontal pocket depth, a major indicator of gum disease. On the phylogenetic tree there was a cluster of microbial species belonging to the genus Corynebacterium that were negatively associated with gum disease, and a cluster of species belonging to the genus Treponema that were positively associated with gum disease. The ratio of these two organisms was highly predictive of disease status and correlated significantly with multiple metrics of gum disease. Members of both Corynebacterium and Treponema have been shown to serve as scaffolds for biofilm formation, so we hypothesize that the increased diversity of microorganisms observed in periodontal disease could be attributed to a shift in the structure and assembly of subgingival biofilms.
Saliva samples are easier to collect that subgingival plaque, so we wanted to test for parallel microbial composition changes in saliva to see if it could be used as an alternative to plaque for future experiments. We processed a subset of matched saliva samples and found that the microbial indicator of gum disease (or ratio between Corynebacterium and Treponema species) was significantly correlated with periodontal pocket depth, but not any of the other metrics for gum disease. These results demonstrate that the shifts in microbial community organization in subgingival pockets are detectable in saliva, but at a lower resolution than plaque itself.
To get back at our original question of how early microbial markers of gum disease may be related to cardiometabolic disease, we probed the relationship of the ratio of Treponema to Corynebacterium to various cardiometabolic metrics. We found that the conversion of biofilm-scaffolding bacteria in subgingival plaque was significantly correlated with higher blood pressure, increased insulin, and the Homeostatic Model Assessment for Insulin Resistance (HOMA-IR). This finding provides evidence for a relationship between periodontal and cardiometabolic etiology.
Follow-up studies are needed to determine if this shift in biofilm composition underlying gum disease is contributing to cardiometabolic shifts through increased microbial entry to the blood stream, a shift in the metabolites produced during chewing in the oral cavity, or some other mechanism. Because this dataset is cross-sectional it does not directly prove a cause-and-effect relationship. We hope to validate the implications of this finding in future longitudinal analyses of ORIGINS as samples continue to be collected from the same individuals over time, so stay tuned!
Bennett J, Lanning S. The netflix prize. InProceedings of KDD cup and workshop 2007 Aug 12 (Vol. 2007, p. 35).
Martino C, Morton JT, Marotz CA, Thompson LR, Tripathi A, Knight R, Zengler K. A novel sparse compositional technique reveals microbial perturbations. MSystems. 2019 Feb 12;4(1):e00016-19.
Morton JT, Marotz C, Washburne A, Silverman J, Zaramela LS, Edlund A, Zengler K, Knight R. Establishing microbial composition measurement standards with reference frames. Nature communications. 2019 Jun 20;10(1):1-1.