SMDI: An Index for Measuring Subgingival Microbial Dysbiosis

Abstract

An intuitive, clinically relevant index of microbial dysbiosis as a summary statistic of subgingival microbiome profiles is needed. Here, we describe a subgingival microbial dysbiosis index (SMDI) based on machine learning analysis of published periodontitis/health 16S microbiome data. The raw sequencing data, split into training and test sets, were quality filtered, taxonomically assigned to the species level, and centered log-ratio transformed. The training data set was subject to random forest analysis to identify discriminating species (DS) between periodontitis and health. DS lists, compiled by various “Gini” importance score cutoffs, were used to compute the SMDI for samples in the training and test data sets as the mean centered log-ratio abundance of periodontitis-associated species subtracted by that of health-associated ones. Diagnostic accuracy was assessed with receiver operating characteristic analysis. An SMDI based on 49 DS provided the highest accuracy with areas under the curve of 0.96 and 0.92 in the training and test data sets, respectively, and ranged from −6 (most normobiotic) to 5 (most dysbiotic) with a value around zero discriminating most of the periodontitis and healthy samples. The top periodontitis-associated DS were Treponema denticola, Mogibacterium timidum, Fretibacterium spp., and Tannerella forsythia, while Actinomyces naeslundii and Streptococcus sanguinis were the top health-associated DS. The index was highly reproducible by hypervariable region. Applying the index to additional test data sets in which nitrate had been used to modulate the microbiome demonstrated that nitrate has dysbiosis-lowering properties in vitro and in vivo. Finally, 3 genera ( Treponema, Fretibacterium, and Actinomyces) were identified that could be used for calculation of a simplified SMDI with comparable accuracy. In conclusion, we have developed a nonbiased, reproducible, and easy-to-interpret index that can be used to identify patients/sites at risk of periodontitis, to assess the microbial response to treatment, and, importantly, as a quantitative tool in microbiome modulation studies.

Early microbial markers of periodontal and cardiometabolic diseases in ORIGINS

“…This log-ratio was used as a MIP. Previous studies have proposed alternative MIPs 24 , 25 , which were also significantly different between health and disease in this dataset, but were not statistically more predictive than the Treponema:Corynebacterium ratio for discriminating disease type. We must acknowledge that identifying microbial biomarkers in next-generation sequencing datasets always carries the risk for false positives, but in this study, we focused on the Treponema and Corynebacterium ASVs because the ability of all the species and strains of these genera to significantly discriminate sites of early disease before clinically meaningful deep pockets formed suggests that these phylogenetic branches have been evolutionarily conserved in subgingival plaque biofilm formation.…”

“…Other microbial indicators of periodontitis have been proposed based on the analysis of chronic periodontitis 24 , 25 . We assessed these alternative microbial ratios from Chen et al 24 , ( Treponema denticola, Mogibacterium timidum, Fretibacterium spp ., and Tannerella forsythia vs. Actinomyces naeslundii and Streptococcus sanguini s; representing 158 vs 238 ASVs, respectively), and Meuric et al 25 , ( Eubacterium, Campylobacter, Treponema , and Tannerella vs. Veillonella, Neisseria, Rothia, Corynebacterium , and Actinomyces ; representing 27 vs. 9 ASVs, respectively) in this dataset. Both of these microbial ratios significantly discriminated healthy from diseased subgingival plaque samples.…”

The Importance of Nitrate Reduction for Oral Health

“…It is therefore important to assess if molecules that can increase the salivary pH, such as nitrate, arginine, and urea (Fig. 4A), could provide a The SMDI index (Chen et al 2021) calculates the degree of dysbiosis based on 16S rRNA sequencing data from a periodontitis point of view. ) from food enters the bloodstream, and plasma nitrate is concentrated into saliva.…”

“…Other genera that include nitrate-reducing representatives (e.g., Veillonella and Actinomyces ) or genera previously associated with nitrate/nitrite metabolism (e.g., Prevotella and Streptococcus ) appear to decrease in the presence of nitrate, and changes may occur at just the species level, which should be explored in future studies. Interestingly, Rothia and Neisseria are genera associated with oral disease–free individuals (i.e., oral health) when compared with patients with caries (Agnello et al 2017; Rosier et al 2018) and periodontal diseases (Meuric et al 2017; Feres et al 2020; Chen et al 2021). Additionally, R. mucilaginosa —the most common Rothia species on the tongue (Rosier et al 2021)—is associated with halitosis-free individuals (Carda-Diéguez et al 2021).…”

“… a The SMDI index (Chen et al 2021) calculates the degree of dysbiosis based on 16S rRNA sequencing data from a periodontitis point of view. …”