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. Author manuscript; available in PMC: 2018 Mar 1.
Published in final edited form as: J Clin Periodontol. 2017 Jan 27;44(3):255–265. doi: 10.1111/jcpe.12664

The Subgingival Microbiome, Systemic Inflammation and Insulin Resistance: The Oral Infections, Glucose Intolerance and Insulin Resistance Study (ORIGINS)

Ryan T Demmer 1, Alexander Breskin 1, Michael Rosenbaum 2, Aleksandra Zuk 3, Charles LeDuc 2, Rudolph Leibel 2, Bruce Paster 4, Moïse Desvarieux 1,5, David R Jacobs Jr 6, Panos N Papapanou 7
PMCID: PMC5328907  NIHMSID: NIHMS837875  PMID: 27978598

Abstract

Background

Inflammation might link microbial exposures to insulin resistance. We investigated the cross-sectional association between periodontal microbiota, inflammation and insulin resistance.

Methods

The Oral Infections, Glucose Intolerance and Insulin Resistance Study (ORIGINS) enrolled 152 diabetes-free adults (77% female) aged 20–55 years (mean=34±10). 304 subgingival plaque samples were analyzed using the Human Oral Microbe Identification Microarray to measure the relative abundances of 379 taxa. C-reactive protein, interleukin-6, tumor necrosis factor-α and adiponectin were assessed from venous blood and their z-scores were summed to create an inflammatory score (IS). Insulin resistance was defined via the HOMA-IR. Associations between the microbiota and both inflammation and HOMA-IR were explored using multivariable linear regressions; mediation analyses assessed the proportion of the association explained by inflammation.

Results

The IS was inversely associated with Actinobacteria and Proteobacteria and positively associated with Firmicutes and TM7 (p-values < 0.05). Proteobacteria levels were associated with insulin resistance (p < 0.05). Inflammation explained 30%–98% of the observed associations between levels of Actinobacteria, Proteobacteria or Firmicutes and insulin resistance (p-values < 0.05). 18 individual taxa were associated with inflammation (p < 0.05) and 22 with insulin resistance (p < 0.05). No findings met Bonferroni-adjusted statistical significance.

Conclusion

Bacterial measures were related to inflammation and insulin resistance among diabetes-free adults.

Keywords/phrases: Inflammation, C-Reactive Protein, Interleukin-6, Tumor Necrosis Factor-α, Adiponectin, Insulin Resistance, Diabetes, Microbiota, Microbiome, Periodontal

Introduction

Chronic systemic inflammation has been linked to increased risk of insulin resistance and the development of type 2 diabetes mellitus (T2DM) (Pradhan et al., 2003, Pradhan et al., 2001, Park et al., 2009). Mechanistically, this might be explained by the ability of inflammatory molecules to interfere with cellular insulin signaling pathways (Hotamisligil et al., 1996). Consequently, there is value in studying modifiable upstream drivers of chronic inflammation in the population such as microbial challenge.

Microbes are known to modulate host immune system function (Round and Mazmanian, 2009, Hajishengallis, 2014) and several monoinfections, including Helicobacter pylori (Jeon et al., 2012), hepatitis C(White et al., 2008) and tuberculosis (Dooley and Chaisson, 2009) have been linked to diabetogenesis. Additionally, periodontitis, a chronic polymicrobial infection that affects the tooth-supporting structures, has been linked to systemic inflammation and T2DM risk. Since periodontitis is characterized by dysbiotic subgingival ecology (Socransky and Haffajee, 2005, Wade, 2013), research linking periodontitis to extra-oral outcomes hypothesizes that subgingival dysbiosis (Palmer, 2014, Cekici et al., 2014, Socransky and Haffajee, 2005, Kistler et al., 2013, Demmer et al., 2008b) is an important etiologic factor (Lalla and Papapanou, 2011, Lockhart et al., 2012, Kebschull et al., 2010, Demmer and Desvarieux, 2006).

Numerous observational and interventional studies have linked periodontitis and systemic inflammation (Paraskevas et al., 2008) (Demmer et al., 2013) (Teeuw et al., 2014). Similarly, a number of observational studies indicate that periodontitis is associated with insulin resistance (Demmer et al., 2012), prediabetes incidence (Saito et al., 2004) and prevalence (Arora et al., 2014), hemoglobin A1c progression (Demmer et al., 2010) and T2DM development (Demmer et al., 2008a); and meta-analyses of intervention studies consistently support the role of anti-infective periodontal therapy in the reduction of hemoglobin A1c (Wang et al., 2014, Teeuw et al., 2010) although not all findings have been positive (Engebretson et al., 2013, Miranda et al., 2014).

The subgingival microbiota has not been broadly characterized in relation to systemic inflammatory phenotype or insulin resistance. While periodontitis is a clinically relevant indicator of dysbiosis, early subgingival shifts towards dysbiosis are associated with gingival inflammation and precede the development of clinical disease (Demmer et al., 2008b, Socransky and Haffajee, 2005). As such, assessments of the microbiota provide an opportunity for early risk assessment and validation of the microbial hypothesis. Accordingly, we have recently studied eleven bacterial species frequently observed in periodontitis to be related to prediabetes in a diabetes-free sample of adults (Demmer et al., 2015). In contrast, the relationship between periodontitis and prediabetes was weak and nonstatistically in the same sample (Demmer et al., 2015). Therefore, broad characterization of the subgingival microbiota in relation to systemic inflammation and diabetes risk has the potential to identify unique microbial signatures linked to early diabetes risk, thereby improving future research designs and informing clinical practice.

In the present work, we have examined the relationship between levels of over 300 subgingival bacteria and markers of systemic inflammation and insulin resistance, concurrently, among diabetes-free adults enrolled in The Oral Infections, Glucose Intolerance and Insulin Resistance Study (ORIGINS). Additionally, formal mediation analyses were conducted to evaluate the role of systemic inflammation as a mediator of the association between the microbiota and insulin resistance. We hypothesized that subgingival microbial signatures would be related to both inflammatory phenotype and measures of insulin resistance and that there would be evidence of mediation by inflammatory markers.

METHODS

ORIGINS is a cohort study among members of union employed at Columbia University, investigating the relationship between subgingival microbial community composition, systemic inflammatory phenotype and impaired glucose metabolism. The ORIGINS sample has been previously described (Demmer et al., 2015). From February 2011–May 2013, 300 men and women were enrolled. Inclusion criteria were as follows: i) aged 20–55 years; ii) no diabetes mellitus (T1 or T2) based on participant self-report of no previously diagnosed disease, HbA1c values < 6.5% and fasting plasma glucose < 126 mg/dl; iii) no history of myocardial infarction, congestive heart failure, stroke, or chronic inflammatory conditions based on participant self-report. The current analysis includes the first 152 participants enrolled among whom comprehensive assessments of the subgingival microbiota were made. The Columbia University Institutional Review Board approved the study protocol. All participants signed written informed consent.

Periodontal Examination

As previously described (Demmer et al., 2015), trained calibrated dental examiners assessed bleeding on probing, probing depth and clinical attachment loss at 6 sites per tooth (mesiobuccal, midbuccal, distobuccal, mesiolingual, midlingual and distolingual) with a UNC-15 manual probe (Hu-Friedy). Periodontitis was defined per the CDC/AAP guidelines as none/mild or moderate/severe. Periodontal examination reliability studies were performed and results have been published (Demmer et al., 2015).

Subgingival Plaque Collection and Bacterial Assessments

In total, 304 subgingival plaque samples (two samples from each of 152 participants) were collected from the 2nd most posterior teeth (excluding 3rd molars) in the upper right and lower left quadrants using sterile curettes, after removal of the supragingival plaque. The samples were suspended in 300 μl of TE buffer (50 mM Tris, 1 mM EDTA; pH 7.6) and microbial DNA was extracted using the MasterPure™ Gram Positive DNA Purification Kit (Epicentre).

Microbial analysis of DNA samples was performed using the Human Oral Microbe Identification Microarray (HOMIM), as previously published (Colombo et al., 2009) and detailed in the Supplemental Materials. Briefly, 16S rRNA-based, reverse-capture oligonucleotide probes targeting 379 bacterial taxa (http://mim.forsyth.org/bacteria.html) were printed on aldehyde-coated glass slides. 16S rRNA genes were PCR amplified using 16S rRNA universal primers (NF1: 50 -CCA GRG TTY GAT YMT GGC-30; 1541R: 50 -RAA GGA GGT GWT CCA DCC-30; 1492R: 50 -GDT AYC GGT GWT CCA DCC-30), and labeled via incorporation of Cy3-dCTP in a second nested PCR (9F: 50 -GRG TTY GAT YMT GGC TCA G-30 and 1492R). The labeled amplicons were hybridized to probes on the slides. Microarray slides were washed and scanned and crude data were extracted using a program for microarray analysis. Signals were normalized by comparing individual signal intensities to the average of signals for the universal probes. Original signals < 2 times the background value were reset to 1 and assigned a value = 0. All the values >1 were categorized into scores 1–5 based on signal intensity.

Assessment of Insulin Resistance and Inflammation

Blood was collected following an overnight fast and plasma glucose and insulin levels were measured. The Homeostasis Model Assessment for Insulin Resistance (HOMA-IR) was derived from fasting insulin and glucose (Matthews et al., 1985). Adiponectin, Tumor Necrosis Factor-α (TNF-α), Interleukin-6 (IL-6) and high-sensitivity C-Reactive Protein (CRP) values were standardized by creating z-scores. An inflammatory score (IS) was created by summing z-scores across analytes (values of adiponectin were multiplied by −1 to reflect its anti-inflammatory nature) as previously described (Demmer et al., 2015).

Risk Factors

Cardiometabolic risk factors were measured by trained research assistants in space provided by a Center for Translational Science Award (CTSA). Participant body mass index (BMI) was calculated as weight in kilograms/height in meters2. Questionnaires were administered to obtain information on: age, sex, race/ethnicity (nonHispanic Black, nonHispanic White, Hispanic, Other), educational level (high school completion, college or vocational training, advanced degrees), cigarette smoking (current, former or never smoking and duration/intensity of smoking).

Statistical Analysis

All analyses were conducted using SAS 9.4 (SAS Institute, Cary NC) or R version 3.2.1.

Bacterial exposures were operationalized as the relative abundance of bacterial phyla (or taxa) by dividing the respective HOMIM signals by the sum of all taxa signals within the individual. Multivariable linear regression models regressed inflammatory score and HOMA-IR (dependent variables) across tertiles of bacterial phyla (independent variables) relative abundance. We also present results for components of the inflammatory score and HOMA-IR. All multivariable regressions were adjusted for the potential confounders using previously described conceptual modeling approaches (Arora et al., 2014).

Formal mediation analyses assessed the potential role of inflammatory score as a mediator of the microbiota-insulin resistance association as follows. The total effect of bacterial exposure was decomposed into the direct effect and the indirect (i.e., inflammation mediated) effect using a series of multivariable linear regression models (Valeri and Vanderweele, 2013, Demmer et al., 2012). Evidence for inflammatory mediation was based on results from regression models combined with additional assumptions described in supplemental materials.

The use of phylum relative abundance as the primary exposure serves two purposes: i) it is a broad phylogenic surrogate measure of community membership that likely correlates, albeit imperfectly, with community function (Langille et al., 2013); and ii) it enables substantial data reduction and the formation of a priori hypotheses for a small set of exposures. The bacterial phyla considered were Actinobacteria, Bacteroidetes, Firmicutes, Fusobacteria, Proteobacteria, Spirochetes, Tenericutes and TM7. Based on previous literature (Abusleme et al., 2013, Griffen et al., 2012), we hypothesized a priori that Actinobacteria and Proteobacteria would be inversely associated with inflammation and insulin resistance while Bacteroidetes, Firmicutes and Spirochetes would be positively associated with inflammation and insulin resistance. Therefore, based on the formulation of a priori, literature-driven directional hypotheses, we use one-tailed hypothesis tests for the five aforementioned phyla. Two-tailed tests were utilized for Fusobacteria, Tenericutes and TM7.

Exploratory analyses were also conducted for the individual taxa using multivariable linear regressions to model inflammatory and metabolic biomarkers as dependent variables and each taxon as an independent variable in separate regressions. Bonferroni adjusted p-values and false discover rates (FDR) were used to account for multiple comparisons although no associations were found have an FDR or Bonferroni corrected p-value of < 0.05 in this relatively small sample. We additionally calculated multivariable adjusted Spearman correlations between each bacterial taxon and inflammatory and metabolic outcomes. Adjusted Spearman correlations were clustered using two-way hierarchical clustering and heat maps were generated to illustrate the various relationships between all taxa and inflammatory and metabolic outcomes.

Sensitivity analyses were conducted using absolute measures of taxa abundance (instead of relative measures) and no meaningful differences were noted so those data are not presented.

RESULTS

General Characteristics of the Cohort

The mean(SD) age of the participants was 35(10) years; 81% were female, 47% were Hispanic, 19% white, 23% black and 11% other. The prevalence estimates of none/mild, moderate and severe periodontitis were 38%, 53% and 9%, respectively. Additional characteristics are summarized in Supplemental Table 1.

Among 379 possible taxa measured via the HOMIM, 313 taxa were detected in at least one participant and 121 taxa were detected in ≥20% of participants. Figure 1a summarizes the distribution of all taxa detected in ≥20% the sample and Figure 1b shows the distribution of phylum-level measures. Firmicutes were the most frequently observed phylum with a relative abundance of 41%, followed by Proteobacteria, Bacteroidetes, Actinobateria and Fusobacteria (Figure 1b). Mean levels of inflammatory values and metabolic outcomes are summarized in Supplemental Table 1. Pairwise correlations among natural log transformed values of the four inflammatory mediators were modest and only the following three were statistically significant: TNF-alpha:IL-6 r=0.16(p=0.04); CRP:IL-6 r=0.33(p < 0.0001); CRP:adiponectin r=−0.17(p=0.04). Mean glucose±SD levels were 86±8 mg/dl and median (25th, 75th percentile) insulin and HOMA-IR values were 9(6, 13) μU/ml and 1.9(1.3, 2.8) respectively.

Figure 1.

Figure 1

Figure 1

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Distribution of the 121 subgingival bacterial taxa detected in at least 20% of subjects (a) and 9 bacterial phyla (b) among n=152 men and women aged 18 – 55 years enrolled in The Oral Infections, Glucose Intolerance and Insulin Resistance Study (ORIGINS) 2011 – 2013.

Inflammation, fasting glucose, insulin and insulin resistance

After multivariable adjustment, the inflammatory score was positively associated with glucose, insulin and HOMA-IR (all p < 0.05, Figure 2). In regression models that simultaneously included CRP, adiponectin, TNF-α and IL-6, CRP were directly associated with insulin and HOMA-IR, while adiponectin was inversely associated with glucose, insulin and HOMA-IR. Additional adjustment for bacterial measures did not change the pattern of relationships and all aforementioned results remained statistically significant.

Figure 2.

Figure 2

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Mean adjusted glucose, insulin and HOMA-IR levels across tertiles of four inflammatory mediators, C-Reactive Protein (CRP), Tumor Necrosis Factor-α (TNF-α), Interleukin-6 (IL-6) and adiponectin, and an inflammatory score based on the sum of the z-scores of the aforementioned mediators. Adjusted for age, sex, race/ethnicity, education, smoking status and body mass index.

Association between Bacterial Phyla, Inflammation and Metabolic Outcomes

The relative abundances of four bacterial phyla were associated with systemic inflammation. Actinobacteria and Proteobacteria levels were both inversely associated with the inflammatory score (p < 0.01 and p=0.03 respectively, Figure 3) while Firmicutes (p=0.001) and TM7 (p=0.05) were positively associated with the inflammatory score (Figure 3, Supplemental Table 2a). CRP and TNF-α levels were increased >300% and 50%, respectively, between the 3rd vs. 1st tertile of Firmicutes (Figure 3). In contrast, TNF-α levels decreased by 20% across tertiles of Actinobacteria, and adiponectin levels increased by 30% across tertiles of Proteobacteria (Figure 3). Levels of Spirochetes, Tenericutes or Fusobacteria were not associated with any of the measured inflammatory mediators or with the IS, while Bacteroidetes levels were inversely associated with only adiponectin (difference between 3rd vs. 1st tertile = −1965 ng/ml, p=0.03). The largest difference between mean glucose levels in the 3rd vs. 1st tertile of any of the eight phyla was −2.2 mg/dl for Proteobacteria (p=0.10, Figure 4) and results for all other phyla were similarly weak and null (data not shown). When comparing 3rd tertile vs. 1st tertile for Proteobacteria, both insulin and HOMA-IR values were decreased by ~20% (Figure 4). In contrast, a comparison of 3rd vs. 1st tertile levels of Firmicutes yielded insulin and HOMA-IR values that were ~20% higher although these results were not statistically significant (Figure 4, Supplemental Table 2b).

Figure 3.

Figure 3

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Mean adjusted levels of four inflammatory mediators (C-Reactive Protein (CRP), Tumor Necrosis Factor-α (TNF-α), Interleukin-6 (IL-6) and adiponectin), and an inflammatory score (sum of the four standardized inflammatory markers) according to tertiles of the bacterial phyla Actinobacteria, Firmicutes and Proteobacteria. Adjusted for age, sex, race/ethnicity, education, smoking status and body mass index.

Figure 4.

Figure 4

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Mean adjusted glucose, insulin and HOMA-IR levels across tertiles of the bacterial phyla Actinobacteria, Firmicutes and Proteobacteria. Adjusted for age, sex, race/ethnicity, education, smoking status and body mass index.

Among the four phyla statistically significantly associated with the inflammatory score for which formal mediation analyses were appropriate, three demonstrated statistically significant mediated effects (Table 1, Supplemental Table 2c). The percentages of the overall phyla/HOMA-IR associations estimated to be mediated via inflammation were 74% for Actinobacteria (p=0.03), 27% for Proteobacteria (p=0.05) and 98% for Firmicutes (p=0.01).

Table 1.

Inflammatory Mediation of the Association between Subgingival Microbiota and Insulin Resistance. 152 men and women aged 18 – 55 years enrolled in The Oral Infections, Glucose Intolerance and Insulin Resistance Study (ORIGINS) 2011 – 2013.

Proteobacteria
Effect Estimate SE T-value P-value
a (Exposure->Mediator) −0.82 0.45 −1.8 0.03
b (Mediator->Outcome) 0.067 0.02 3.29 0.001
c (Total Effect) −0.21 0.11 1.83 0.02
c′(Direct Effect) −0.15 0.11 1.37 0.09
ab (Mediated Effect) −0.05 0.03 −1.57 0.05
Proportion Mediated = 27%
Actinobacteria
Effect Estimate SE T-value P-value
a (Exposure->Mediator) −0.99 0.42 −2.38 0.01
b (Mediator->Outcome) 0.07 0.02 3.44 0.0008
c (Total Effect) −0.10 0.11 −0.9 0.19
c′(Direct Effect) −0.02 0.10 −0.24 0.41
ab (Mediated Effect) −0.07 0.04 −1.96 0.03
Proportion Mediated = 74%
Firmicutes
Effect Estimate SE T-value P-value
a (Exposure->Mediator) 1.38 0.45 3.07 0.001
b (Mediator->Outcome) 0.07 0.02 3.47 0.0007
c (Total Effect) 0.11 0.12 0.89 0.19
c′(Direct Effect) 0.002 0.12 0.02 0.49
ab (Mediated Effect) 0.10 0.04 2.30 0.01
Proportion Mediated = 98%
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Estimates derived from linear regression analyses modeling phylum-level relative abundance as the exposure, inflammatory score as the mediator and homeostatic model assessment for insulin resistance (HOMA-IR) as the outcome.

a, b, c, c′, ab as described in Makinnon, 2008 (MacKinnon, 2008)

a is the regression coefficient summarizing the mean difference in inflammatory score between 3rd vs. 1st tertiles of bacterial exposure;

b is the regression coefficient summarizing the mean difference in HOMA-IR between 3rd vs. 1st tertile of the inflammatory score, adjusted for bacterial exposure;

c is the regression coefficient summarizing the mean difference in HOMA-IR between 3rd vs. 1st tertiles of bacterial exposure IR without adjustment for the inflammatory score (i.e., the total effect);

All regressions are multivariable adjusted for age, sex, race/ethnicity, education level, smoking status and body mass index. P-values are one-tailed based on a priori hypotheses (see methods)

Association between Bacterial Taxa, Inflammation and Insulin Resistance

Associations between individual taxa and several inflammatory and metabolic outcomes are summarized in Figure 5. The strongest observed correlation between a bacterial taxon and a biomarker was r=0.28 (p=0.001) for IL-6 and Streptococcus parasanguinis|HOT-721/Streptococcus parasanguinis II HOT-411/Streptococcus sp. HOT-057_R17. The strongest positive and inverse correlations between bacterial taxa and the inflammatory score were for Eubacterium[11][G-3] brachy HOT-557_AC03 r=0.21 (p=0.01) and Actinomyces sp. HOT-170_T61 r=−0.2 (p=0.02), respectively. The strongest positive and inverse correlations between bacterial taxa and HOMA-IR were for Prevotella sp. HOT-299_AH07, r=0.21 (p=0.01) and Actinomyces sp. HOT-170_T61, r=−0.25 (p=0.003).

Figure 5.

Figure 5

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Heatmap of multivariable adjusted Spearman correlation coefficients summarizing the association between 25 subgingival taxa (y-axis) and both inflammatory and metabolic biomarkers (x-axis). Axes were organized via unsupervised hierarchical clustering of correlation coefficients. The top 25 strongest correlations coefficients are presented. Color-coding adjacent to the vertical dendrogram corresponds to phylum membership of the taxa for that row of the heatmap: red = Actinobacteria, blue = Bacteroidetes, green = Firmicutes, purple = Fusobacteria, orange = Proteobacteria, yellow = Spirochetes, pink = Tenericutes, brown = Synergistetes, grey = TM7.

CRP=C-reactive protein, TNF-α=tumor necrosis factor-α.

Results from multivariable linear regression analysis contrasting adjusted mean levels of inflammatory score or HOMA-IR between 3rd vs. 1st tertiles of taxa were consistent with correlation analysis. The total numbers of individual taxa associated with either the inflammatory score or HOMA-IR were 18 and 22, respectively (p < 0.05 for all 3rd vs. 1st tertile contrasts, Supplemental Tables 3a & 3b). Among the 18 taxa associated with the inflammatory score, the following six taxa also demonstrated a statistically significant inflammation-mediated effect on HOMA-IR (Supplemental Table 3c): Parvimonas micra HOT-111_AG78, Parvimonas micra HOT-111_V05, Streptococcus parasanguinis I HOT-721/Streptococcus parasanguinis II HOT-411/Streptococcus sp. HOT-057_R17, Peptostreptococcaceae[13][G-1] sp. HOT-113_AG82 (all four with membership in the phylum Firmicutes), Fretibacterium Cluster_D70 (phylum membership Synergistetes) and Cardiobacterium hominis HOT-633_O97 (phylum membership Proteobacteria). Higher levels of Cardiobacterium hominis HOT-633_O97 were associated with lower levels of inflammation and insulin resistance while the remaining five species were positively associated with these outcomes. None of the aforementioned associations for individual taxa were statistically significant after Bonferroni correction for multiple comparisons.

DISCUSSION

In the current work, we demonstrate that the relative abundance of subgingival phyla and taxa were associated with both systemic inflammation and insulin resistance. In formal mediation analyses, Actinobacteria, Proteobacteria and Firmicutes all demonstrated statistically significant evidence for inflammatory mediation suggesting that 30%–98% of the observed phyla-insulin resistance associations might be explained by inflammation. Exploratory analysis at the taxa level identified several individual taxa to be correlated positively with HOMA-IR, CRP and IL-6 levels, but less so with TNF-α while correlations between bacterial taxa and adiponectin were inverse. However, these findings lacked statistical significance after Bonferroni adjustment for multiple comparisons in this relatively small sample and require confirmation in larger studies.

The observation that phylum-level measures of the subgingival microbiota were related to systemic inflammation and insulin resistance is consistent with previous studies of the subgingival microbiota in relation to periodontitis that have shown Firmicutes (Abusleme et al., 2013) to be overly abundant in periodontitis, while Actinobacteria (Abusleme et al., 2013) and Proteobacteria (Griffen et al., 2012) are overly abundant in periodontal health. These findings are also consistent with preliminary reports concerning the relationship between gut microbiota and both obesity and T2DM. A higher ratio of Firmicutes to Bacteroidetes has been observed among obese vs. lean humans and mice (Ley et al., 2006), and higher levels of taxa from the Firmicutes phyla, including several Clostridum spp., are overly abundant among individuals with T2DM(Wang and Jia, 2016).

Our results at the taxa level are also consistent with findings from other samples considering enriched taxa in periodontitis or periodontal health. Specifically, the following taxa were positively associated with either insulin resistance or inflammation and also have been reported to be overly abundant in periodontitis: Parvimonas micra (formerly Micromonas micros and Peptostreptococcos micros) (Colombo et al., 2009), Peptostreptococcaceae sp. and Fretibacterium spp.(Lourenco et al., 2014, You et al., 2013, Marchesan et al., 2015, Park et al., 2015) and Treponema denticola (Socransky et al., 1998, Demmer et al., 2008b). Conversely, greater abundance of Propionibacterium propionicum HOT-739_AB71 and Actinomyces sp have been shown to be associated with periodontal health (Colombo et al., 2009).

Aggregatibacter actinomycetemcomitans, P. gingivalis, T. denticola and T. forsythia, species traditionally believed to play a pathogenic role in periodontitis (Socransky and Haffajee, 2005), were not associated with inflammation or insulin resistance in this study and we observed inverse associations between T. denticola and insulin resistance. These may be chance findings or might imply a lack of direct involvement for these species in potential mechanisms connecting subgingival microbiota to systemic outcomes, despite being important for local pathology. Alternatively, the abundance of these specific organisms might increase in response to host changes (e.g., overt hyperglycemia or periodontal disease) and only become relevant to systemic inflammation and insulin resistance at more advanced stages of periodontal disease and dysglycemia. Our previous findings from the complete ORIGINS baseline did find Aggregatibacter actinomycetemcomitans, P. gingivalis, T. denticola and T. forsythia levels (assessed via DNA-DNA checkerboard hybridization) to be positively associated with prevalent prediabetes (Demmer et al., 2015). However, these species were not associated with insulin resistance or inflammation in the complete ORIGINS sample, which is consistent with our current findings in a subset of participants who received more comprehensive microbial assessments via HOMIM.

The strong inverse associations observed for Cardiobacterium hominis may appear counterintuitive, as it is a causal organism for infective endocarditis. However, recent reports have shown Cardiobacterium hominis levels also to be similarly inversely related to periodontitis and rheumatoid arthritis (Griffen et al., 2012, Lourenco et al., 2014, Zhang et al., 2015) further supporting its enrichment in situations of good oral and general health.

The current findings advance our understanding of the relationship between periodontal status, systemic inflammation and risk of T2DM. To our knowledge, this is the first study that has broadly characterized the subgingival microbiota in relation to systemic inflammation and insulin resistance, all assessed concurrently. While previous studies have found CRP(Winning et al., 2015) and phospholipase A2 (Boillot et al., 2015) levels to be associated with specific members of the subgingival microbiota, those studies only assessed a small number of bacterial species and were conducted in subjects who were substantially older, included participants with pre-existing T2DM and a higher prevalence of severe periodontitis.

We have only characterized the subgingival microbiota using a microarray and not with more current next generation sequencing technologies, which would have enabled broader detection of taxa and/or direct evaluations of community functional capacity. Defining our microbial exposures at the phylum level also has several disadvantages. There is a broad range of genetic diversity, virulence properties and metabolic activity among organisms within a given phylum, and several of the phyla explored contain a mixture of organisms believed to be pathogenic as well as commensals (e.g., both C. hominis and A. actinomycetemcomitans are Proteobacteria but are associated with periodontal disease in opposite directions (Lourenco et al., 2014, Socransky et al., 1998)).

These data are cross-sectional and cannot infer temporality of associations between the microbiota, inflammatory markers and insulin resistance, and it is possible that glucose and insulin levels in the clinically normal range might have influenced the subgingival microbiota, rather than the reverse, as previous suggested in the context of periodontitis risk (Timonen et al., 2011). However, the host-microbe relationship is complex and host response to individual species and/or dysbiotic communities is likely dynamic and rapidly evolves complicating precise temporal ordering in human studies where large numbers of individuals cannot be feasibly monitored on the necessary time-scale. Longitudinal results from ORIGINS can inform whether baseline microbial measures can predict longitudinal change in inflammatory burden and insulin resistance. It is also possible that genetic factors confound the observed associations. Recent evidence suggests that host genetics might modestly alter the gut (Goodrich et al., 2014) and oral microbiota (Mason et al., 2013), although evidence in specific regard to the influence of host genetics on the subgingival microbiota is limited (Nibali et al., 2016, Divaris et al., 2012). Given the results from several randomized intervention trials showing that anti-infective periodontal therapy reduces markers of systemic inflammation (Demmer et al., 2013), it is plausible that genetics do not completely explain our current findings.

We have found variation in the subgingival microbiota to be associated with levels of systemic inflammation and insulin resistance. Our results support the hypothesis that the systemic inflammatory phenotype possibly mediates the association between subgingival dysbiosis and diabetes risk. While these findings did not meet strict Bonferroni adjusted statistical significance and require replication and confirmation in longitudinal and interventional settings, they provide important insights that further our understanding of hypotheses linking microbial exposures to diabetes risk and can guide the design of future studies.

Supplementary Material

Supp FigS1
Supp MaterialS1
Supp Table S1-S3

Clinical Relevance.

Scientific Rationale

Subgingival dysbiosis is hypothesized as an inflammatory stimulus that might lead to subsequent insulin resistance.

Principal Findings

Bacteria in the phyla Actinobacteria and Proteobacteria were associated with less systemic inflammation while those in the phyla Firmicutes and TM7 were associated with more inflammation. Proteobacteria levels were also associated with reduced insulin resistance and a sizable proportion of the observed associations was explained by inflammation.

Practical Implications

Subgingival bacterial measures are associated with inflammation and insulin resistance among diabetes-free individuals suggesting that the association between periodontal disease and glucose regulation might begin prior to diabetes development.

Acknowledgments

FUNDING

This research was supported by NIH grants R00 DE018739, R21 DE022422 and R01 DK 102932 to Dr. Demmer. Dr. Demmer also received funding from a Calderone Research Award, Mailman School of Public Health, and a Pilot & Feasibility Award from the Diabetes and Endocrinology Research Center, College of Physicians and Surgeons (DK-63608 to Dr. Leibel). This publication was also supported by the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant Number UL1 TR000040, formerly the National Center for Research Resources, Grant Number UL1 RR024156. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

We thank the following individuals for their invaluable contributions to this research: the 1199 SEIU, HS-3/SSA Area leadership including Ms. Consuelo Mclaughin, Mr. Bennett Batista, Mr. Victor Rivera; Ms. Romanita Celenti for her efforts in performing phlebotomy and processing and analyzing plaque samples; Drs. Nidhi Arora, Ashwata Pokherel, Publio Silfa & Thomas Spinell for their skilled examinations and essential participant engagement. We are also profoundly grateful to the ORIGINS participants, for their participation in this research.

Footnotes

Footnotes corresponding to taxa abbreviations:

1Gemella haemolysans HOT-626/Gemella sanguinis HOT-757_K63

2Fusobacterium nucleatum ss animalis HOT-420_AG50

3Streptococcus parasanguinis I HOT-721/Streptococcus parasanguinis II HOT-411/Streptococcus sp. HOT-057_R17

4Campylobacter concisus HOT-575/Campylobacter rectus HOT-748_T86

5Capnocytophaga ochracea HOT-700/Capnocytophaga spp. HOT-323,326,864_Y83

6Streptococcus oralis HOT-707/Streptococcus sp. HOT-064_F46

7Streptococcus salivarius HOT-755/Streptococcus vestibularis HOT-021_AH39

8Lactobacillus gasseri HOT-615/Lactobacillus johnsonii HOT-819_V86

CONFLICT OF INTEREST

The authors have stated explicitly that there are no conflicts of interest in connection with this article.

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