Distinguishing the Signals of Gingivitis and Periodontitis in Supragingival Plaque: a Cross-Sectional Cohort Study in Malawi

Abstract

Periodontal disease ranges from gingival inflammation (gingivitis) to the inflammation and loss of tooth-supporting tissues (periodontitis). Previous research has focused mainly on subgingival plaque, but supragingival plaque composition is also known to be associated with disease. Quantitative modeling of bacterial abundances across the natural range of periodontal severities can distinguish which features of disease are associated with particular changes in composition. We assessed a cross-sectional cohort of 962 Malawian women for periodontal disease and used 16S rRNA gene amplicon sequencing (V5 to V7 region) to characterize the bacterial compositions of supragingival plaque samples. Associations between bacterial relative abundances and gingivitis/periodontitis were investigated by using negative binomial models, adjusting for epidemiological factors. We also examined bacterial cooccurrence networks to assess community structure. The main differences in supragingival plaque compositions were associated more with gingivitis than periodontitis, including higher bacterial diversity and a greater abundance of particular species. However, even after controlling for gingivitis, the presence of subgingival periodontitis was associated with an altered supragingival plaque. A small number of species were associated with periodontitis but not gingivitis, including members of Prevotella, Treponema, and Selenomonas, supporting a more complex disease model than a linear progression following gingivitis. Cooccurrence networks of periodontitis-associated taxa clustered according to periodontitis across all gingivitis severities. Species including Filifactor alocis and Fusobacterium nucleatum were central to this network, which supports their role in the coaggregation of periodontal biofilms during disease progression. Our findings confirm that periodontitis cannot be considered simply an advanced stage of gingivitis even when only considering supragingival plaque.

Importance: Periodontal disease is a major public health problem associated with oral bacteria. While earlier studies focused on a small number of periodontal pathogens, it is now accepted that the whole bacterial community may be important. However, previous high-throughput marker gene sequencing studies of supragingival plaque have largely focused on high-income populations with good oral hygiene without including a range of periodontal disease severities. Our study includes a large number of low-income participants with poor oral hygiene and a wide range of severities, and we were therefore able to quantitatively model bacterial abundances as functions of both gingivitis and periodontitis. A signal associated with periodontitis remains after controlling for gingivitis severity, which supports the concept that, even when only considering supragingival plaque, periodontitis is not simply an advanced stage of gingivitis. This suggests the future possibility of diagnosing periodontitis based on bacterial occurrences in supragingival plaque.

FIG 1

The PCoA ordination of supragingival plaque samples shows an approximate trend with gingivitis severity that is robust to analysis methods. PCoA ordinations based on Bray-Curtis dissimilarities between samples for 626 HOMD OTUs (a, b) and 502 MED phylotypes (c, d). Filled ellipses show mean values for each gingivitis severity, ranging from 0 (yellow) to 6 (dark red). In both cases, an approximate trend is visible, despite the noisiness of the data set. Before plotting, samples were rarefied to 5,000 reads to minimize the impact of sequencing depth.

FIG 2

Microbial community richness and Shannon index increase with gingivitis severity. Both richness (number of observed species) (a) and Shannon index (measure of diversity) (b) of supragingival plaque increase with gingivitis severity. Estimates for each sample were calculated by sampling with replacement at a rarefaction depth of 5,000 sequences per sample and averaging over 100 iterations. The fitted line shows a local polynomial regression fit calculated using loess in R, with the gray region indicating the 95% CI. A total of 138/965 samples were excluded due to having fewer than 5,000 sequences. Changing the rarefaction depth did not affect the conclusion that gingivitis severity was associated with an increase in both species richness and Shannon index.

FIG 3

Summed percentage abundances of OTUs associated with decreased gingivitis (a), increased gingivitis (b), absence of periodontitis (c), and presence of periodontitis (d) for each periodontal disease category. For plotting purposes, samples were rarefied to 10,000 reads per sample, resulting in the removal of 269/962 samples; this rarefaction was not used in the selection of the OTUs, which was performed using DESeq2 on the whole data set. One outlier and two outliers in panels c and d, respectively, are not shown due to trimming the y axis at a relative abundance of 30%.

FIG 4

The cooccurrence network of periodontitis-associated bacteria shows a distinct community structure with the presence of periodontitis across gingivitis severities. (a) The strongly connected central cooccurrence network of periodontitis-associated bacteria across supragingival plaque samples from n = 110 women with severe gingivitis (BoP = 6) and periodontitis. Shown here are significant strong pairwise Spearman correlation coefficients (P < 0.01; ρ > 0.405), calculated with SparCC between MED phylotypes with >98.5% similarity to periodontitis-associated HOMD OTUs (see Materials and Methods). Node color indicates taxonomic genus, size is proportional to log-transformed mean relative abundance, and edge weight indicates the strength of the correlation. The red circle indicates the node with the highest betweenness centrality, classified taxonomically as Fusobacterium nucleatum subsp. vincentii. Node layout was determined using the Fruchterman-Reingold algorithm in qgraph v1.3.1. Twenty-two nodes without any strong correlations connecting them to the rest of the network (i.e., no edges with a ρ of >0.405) were removed during figure preparation. (b) Clustering using hclust in R of the correlation matrices calculated in this way for all severities of periodontal disease. The periodontitis-associated cooccurrence network is more similar between women with periodontitis, regardless of gingivitis severity. Correlation matrices were not adjusted for significance due to the different numbers of women between groups.