Received October 6, 2010; Last revision December 17,
2010; Accepted January 18, 2011
A supplemental appendix to this article is published elec-
tronically only at http://jdr.sagepub.com/supplemental.
© International & American Associations for Dental Research
J.S. Kinney1, T. Morelli1, T. Braun2,
C.A. Ramseier1,3, A.E. Herr4,
J.V. Sugai1, C.E. Shelburne6,
L.A. Rayburn1, A.K. Singh5,
and W.V. Giannobile1,7*
1Department of Periodontics and Oral Medicine, Michigan
Center for Oral Health Research, University of Michigan
School of Dentistry, 24 Frank Lloyd Wright Dr., Lobby M,
Box 422, Ann Arbor, MI 48106 USA; 2Biostatistics Department,
School of Public Health, University of Michigan, 1420
Washington Heights, Ann Arbor, MI 48109-2029,
Medicine, University of Bern, Switzerland; 4Department of
Bioengineering, University of California at Berkeley, Berkeley,
CA 94720, USA; 5Biosystems Research Department, Sandia
National Laboratories, MS 9292, PO Box 969, 7011 East
Avenue, Livermore, CA 94551-0969, USA; 6Department of
Biologic and Material Sciences, School of Dentistry, University
of Michigan, 1210 Eisenhower Place, Ann Arbor, MI 48108,
USA; and 7Department of Biomedical Engineering, College of
Engineering, University of Michigan, Ann Arbor, MI, USA;
*corresponding author, firstname.lastname@example.org
3Department of Periodontology, School of Dental
J Dent Res 90(6):752-758, 2011
The purpose of this study was to determine the role of saliva-
derived biomarkers and periodontal pathogens during periodon-
tal disease progression (PDP). One hundred human participants
were recruited into a 12-month investigation. They were seen
bi-monthly for saliva and clinical measures and bi-annually for
subtraction radiography, serum and plaque biofilm assessments.
Saliva and serum were analyzed with protein arrays for 14 pro-
inflammatory and bone turnover markers, while qPCR was used
for detection of biofilm. A hierarchical clustering algorithm was
used to group study participants based on clinical, microbio-
logical, salivary/serum biomarkers, and PDP. Eighty-three indi-
viduals completed the six-month monitoring phase, with 44
exhibiting PDP, while 39 demonstrated stability. Participants
assembled into three clusters based on periodontal pathogens,
serum and salivary biomarkers. Cluster 1 members displayed
high salivary biomarkers and biofilm; 82% of these individuals
were undergoing PDP. Cluster 2 members displayed low biofilm
and biomarker levels; 78% of these individuals were stable.
Cluster 3 members were not discriminated by PDP status; how-
ever, cluster stratification followed groups 1 and 2 based on
thresholds of salivary biomarkers and biofilm pathogens. The
association of cluster membership to PDP was highly significant
(p < 0.0002). The use of salivary and biofilm biomarkers offers
potential for the identification of PDP or stability (ClinicalTrials.
gov number, CT00277745).
KEY WORDS: periodontal disease, pro-inflammatory
biomarkers, saliva, periodontal pathogens, diagnosis, salivary
products, host-derived local inflammatory mediators are triggered and over-
expressed (Darveau, 2010). In turn, a cascade of events leading to the clinical
signs and deleterious effects of periodontal disease is activated (Kornman,
2008). Currently, periodontal diagnostic methods are limited to the evaluation
of only parameters assessing periodontal destruction. Despite their ease of
use, these parameters fail to provide a real-time assessment of the disease and
offer limited, if any, prognostic value (Giannobile et al., 2009).
Supplemental qualitative and quantitative diagnostic assessment tools have
been developed using saliva for disease recognition and prediction. Oral-fluid-
based tests have detected the presence of periodontopathogens and their associ-
ated host-derived enzymes, inflammatory mediators, and tissue breakdown
products (Eley and Cox, 1996; Loesche et al., 1997; Bassim et al., 2008).
However, given the complex nature of periodontal disease, it is unlikely that a
sole biomarker exists for disease detection and disease prediction (Taba et al.,
2005; Loo et al., 2010). Our group recently identified and reported clusters
of salivary analytes that have the capability to differentiate disease status
accurately (Ramseier et al., 2009).
Despite advancements made in the areas of periodontal disease diagnosis,
only limited longitudinal studies have been conducted to identify biomarkers
that predict disease progression (PDP) prior to radiographic and clinical
manifestations. The objective of this investigation was to determine the
release profile of saliva-derived biomarkers and periodontal pathogens during
PDP longitudinally in a cohort of individuals with periodontitis. A secondary
objective was to determine the ability of the saliva and serum biomarkers to
identify sites associated with PDP.
eriodontal disease is a mixed oral infection initiated by a milieu of virulent
subgingival bacteria. Once exposed to pathogenic bacteria and their by-
MATERIAlS & METHODS
Study Population and Measures of Periodontal Disease Progression
This clinical trial was approved by the University of Michigan Health Sciences
Institutional Review Board and was registered with the NIH clinical trials reg-
istry (ClinicalTrials.gov NCT00277745). After they provided written consent,
100 individuals aged 18 yrs and older were evaluated at the Michigan Center
for Oral Health Research between 2005 and 2007. The baseline characteristics
of this population have been previously reported (Ramseier et al., 2009).
All individuals possessed at least 20 teeth and had not received periodontal
Signatures and Periodontal
J Dent Res 90(6) 2011 Pathogen and Host-response Profiling for Periodontitis 753
treatment or antibiotic therapy for medi-
cal or dental reasons for 3 mos prior to the
investigation. Individuals were excluded
if they possessed a history of metabolic
bone diseases, autoimmune diseases,
unstable diabetes, or post-menopausal
osteoporosis. Pregnant or lactating
women were excluded from participating
in the study (Appendix Table 1).
Participants were enrolled into either a
healthy/gingivitis ora periodontitis group.
The healthy/gingivitis population exhib-
ited < 3 mm of clinical attachment loss
(CAL), no probing depth (PD) of > 4 mm,
and no radiographic alveolar bone loss.
Individuals with periodontitis exhibited a
minimum of 4 sites with evidence of
radiographic bone loss, a minimum of 4
sites with CAL > 3 mm, and a minimum of
4 sites with PD > 4 mm. Participants were
further divided into subgroups based on
clinical parameters. Within the low-risk
group, individuals with bleeding upon
probing (BOP) ≤ 20% were categorized as
healthy, and those with BOP > 20% were
considered to have gingivitis. Individuals
in the disease-susceptible group with ≤
30% of sites with CAL > 3 mm were clas-
sified as having mild chronic periodontitis,
and those with > 30% of sites with CAL
> 3 mm were considered to have moder-
ate-severe periodontitis (Armitage, 1999;
Tonetti and Claffey, 2005).
Participants were seen bi-monthly over
a 12-month period (Fig. 1). To identify
disease progression, we collected data dur-
ing two phases; the disease-monitoring
phase (baseline to 6 mos) and the disease-
recovery phase (6-12 mos). During the
disease-monitoring phase, no periodontal
treatment was provided. Individuals in the
low-risk group received a maintenance
prophylaxis and OHI at 6 and 12 mos.
Those in the disease-susceptible group
received scaling and root planing and OHI
at 6 mos and maintenance prophylaxis at
each of the remaining study visits. Tobacco cessation was not pro-
vided during the study. Rescue therapy consisting of localized scal-
ing and root planing was provided at any study visit if a site
underwent an increase in clinical attachment loss of > 2 mm from
the baseline measures. Clinical and radiographic measures, as well
as calibration and training protocols for these measures, are
described in the Appendix.
Whole Saliva Collection and Analysis
Unstimulated whole saliva was collected at each study visit via
passive drooling into sterile plastic tubes from all participants
(Mandel and Wotman, 1976). Samples were placed on ice, sup-
plemented with a proteinase inhibitor combination of 1% apro-
tinin and 0.5% phenylmethylsulphonylfluoride, and aliquotted
prior to storage at -80°C. Specific information regarding indi-
vidual protein arrays and ELISA assessment of saliva and serum
(below) biomarkers associated with matrix destruction, inflam-
mation, host response, and bone turnover are described in the
Serum Collection and Analysis
A 20-mL quantity of whole blood was collected at baseline and
6 and 12 mos. Once collected, samples were allowed to clot at
Figure 1. Study timeline and recruitment/enrollment activities of the study. (A) Study timeline.
(b) The study population was stratified into four groups.
754 Kinney et al. J Dent Res 90(6) 2011
room temperature for 30 min, then centrifuged for 15 min at
2600 rpm. Serum was stored at -80°C until analysis. The analy-
sis of the identified serum biomarkers used for analysis and their
measurements are described in the Appendix.
Chain-reaction (qPCR) Microbial Plaque biofilm
Subgingival plaque biofilm was collected from the mesiobuccal
aspect of all teeth at baseline, 6, and 12 mos as described previously
(Shelburne et al., 2008). The detection of Porphyromonas gingiva-
lis, Prevotella intermedia, Tannerella forsythia, Fusobacterium
nucleatum, Treponema denticola, and Campylobacter rectus was
quantified by qPCR as described previously (Mullally et al., 2000).
Samples were pooled, after which we calculated the percentage of
the total flora for each species by dividing the number of target
organisms by the total number of bacteria as determined by qPCR,
using 16S rRNA primers that reacted with all bacterial species.
Data were represented on a participant-based assessment.
Participant characteristic differences (Appendix Table 2) by
initial periodontal health were assessed with a Kruskal-Wallis
test for continuous characteristics and a chi-squared test of asso-
ciation for binary characteristics. Clinical periodontal measures
were first averaged within-mouth before being analyzed.
Longitudinal patterns of clinical periodontal measures, biofilm
pathogen levels, and salivary and serum biomarkers were sum-
marized by means and standard errors at each time-point. The
(robust) standard errors for each time-point were adjusted for
within-subject correlation (repeated measures) through the use of
generalized estimating equations (GEE) with a working indepen-
dence correlation matrix. Further explanation of the statistical
methods used is provided in the Appendix.
Study timeline and recruitment/enrollment activities of the
study are shown in Fig. 1. In total, 148 individuals were
screened. Forty-eight failed the clinical screening, leaving 100
participants who were stratified into four groups according to
their clinical criteria. During disease progression analysis, 83
participants completed the study, with 44 exhibiting PDP during
the disease-monitoring period, while 39 demonstrated stability.
Characteristics of individuals at baseline were previously
presented (Ramseier et al., 2009), and those completing the
investigation are described in Appendix Table 2. Clinical data
were significantly different among the four groups for mean
number of teeth, BOP, % GRI, % sites with plaque, mean PD, %
sites PD > 4 mm, mean CAL, and mean BL (all p ≤ 0.001). The
prevalence of smoking was significantly higher in participants
with mild and moderate-severe chronic periodontitis (38% and
80%, respectively) compared with healthy individuals and those
with gingivitis (0% and 21%, respectively). Between-group dif-
ferences were significant at p < 0.001. The percentage of males
in each of the study groups ranged from 35% to 60% (p =
0.325). In addition, the percentage of Caucasian participants in
the study groups ranged from 67% to 87% (p = 0.489).
Furthermore, the mean age of the study population ranged from
46 to 54 yrs (p = 0.735) (Appendix Table 2).
Periodontal data are shown in Fig. 2. To identify disease
progression, we collected data at two different phases: the dis-
ease-monitoring phase (baseline-6 mos) and the disease-recov-
ery phase (6-12 mos). During the disease-monitoring phase,
there were no significant changes regarding periodontal disease
measurements. However, during the disease-recovery phase,
participants with periodontal disease showed a significant mean
PD reduction at 12 mos compared with baseline (p < 0.01).
Furthermore, those with gingivitis and periodontitis demon-
strated significant gains in CAL and % sites with BOP (p < 0.01)
at 12 mos. Participants with mild chronic periodontitis revealed
significant radiographic bone gain at 12 mos compared with
baseline (p < 0.05). During the disease-monitoring phase, rescue
therapy was provided to seven participants (8 sites) in the mild
periodontitis group and nine (36 sites) in the moderate-severe
The longitudinal plots of salivary biomarker levels found
among the four groups over 12 mos are shown in Fig. 3. During
the disease-monitoring phase, the salivary biomarker levels did
not reveal significant differences compared with baseline.
During the disease-recovery phase, individuals with moderate-
severe periodontitis demonstrated reduction of salivary bio-
marker levels at 12 mos compared with baseline, specifically,
MMP-8, MMP-9, OPG, and IL-1β (p < 0.05). Healthy individu-
als and those with periodontitis demonstrated significant
increases in calprotectin at 12 mos compared with baseline (p <
0.05). Regarding serum biomarker expression, all participants
demonstrated a reduced expression of MMP-8 and MMP-9 (p <
0.05) and a significant increase of serum OPG and calprotectin
(p < 0.05) at 12 mos compared with baseline (Appendix Fig. 1).
Regarding the percentage of periodontal pathogens found
among the four groups over 12 mos, there were no significant find-
ings during the disease-monitoring phase. However, during the
disease-recovery phase, participants with chronic periodontitis
demonstrated a significant reduction of all periodontal pathogens
at 12 mos compared with baseline (p < 0.05). In addition, healthy
individuals and those with gingivitis showed significant reductions
in F. nucleatum (p < 0.001) at 12 mos (Appendix Fig. 2).
The hierarchical clustering used to determine the role of peri-
odontal pathogens, salivary, and serum biomarkers on PDP is
presented in Fig. 4. PDP was defined as at least 2 sites with > 2
mm of CAL loss during the disease-monitoring phase. Participants
were divided into three clusters (progression, indeterminate, and
stable) based on the disease progression evaluated during the dis-
ease-monitoring phase. Thirty-four individuals were classified
under the progression cluster. Periodontal pathogens and salivary
and serum biomarkers were able to predict PDP for 24 of these 34
individuals categorized by clinical disease progression. Regarding
the stable cluster, periodontal pathogens and salivary and serum
biomarkers were able to predict clinical stability for 25 of 34 indi-
viduals. Sixteen individuals were assigned to the ‘indeterminate’
J Dent Res 90(6) 2011 Pathogen and Host-response Profiling for Periodontitis 755
cluster. To elucidate the significance of
periodontal pathogens and salivary and
serum biomarkers on the indeterminate
cluster, we performed an additional hierar-
chical clustering (Appendix Fig. 3).
Analysis of Cluster 3 demonstrated that
individuals were further divided into simi-
lar subdivisions as Clusters 1 and 2,
but simply the thresholds of change for
saliva and plaque biomarkers were lower
(Appendix Fig. 3).
Periodontitis is a chronic disease com-
posed of a group of inflammatory condi-
tions affecting the supporting struc-
tures of the dentition (Armitage, 1999).
Traditional periodontal diagnostic meth-
ods are limited to the evaluation of
parameters that assess only periodontal
destruction. Development of innovative
diagnostic tests enabling active phases of
periodontal disease to be detected and
identifying individuals at risk for future
disease occurrence is the focus of numer-
ous clinical investigations.
As previously described by our group,
analysis of our data identified putative
biomarkers from saliva and anaerobic
pathogens that were strongly related to
disease status (Ramseier et al., 2009).
Among the salivary biomarkers, IL-1β,
MMP-8, MMP-9, and OPG demonstrated
the highest correlation with disease status.
Further, the use of multiple time-points of
two-month intervals of saliva biomarkers
allows for an improved understanding of
biomarker fluctuations over time.
During periodontal disease, host
inflammatory cells are recruited, and
inflammatory cytokines, such as IL-1β,
IL-6, and TNF-α, are released from fibro-
blasts, macrophages, connective tissue,
and junctional epithelial cells. As a result,
host-derived enzymes, such as MMP-8,
MMP-9, and calprotectin, are released by
PMNs and osteoclasts, leading to connec-
tive tissue and alveolar bone degradation.
Currently, studies have demonstrated
the association of host-response salivary
biomarkers and periodontal pathogens
with periodontal disease (Herr et al.,
2007; Gursoy et al., 2009; Ramseier et al.,
2009; Teles et al., 2010). However, there
is a gap in the literature regarding longitu-
dinal studies in this area. To the best of our knowledge, this
study is unique in that it provides a longitudinal analysis of
host-response biomarkers and periodontal pathogens during the
course of periodontal disease progression and recovery.
Figure 2. Longitudinal plots of mean (± SD) clinical periodontal measures stratified by initial
category of periodontal health. Compared with baseline, individuals in the mild and moder-
ate/severe periodontitis groups showed significant mean PD reductions at 8, 10, and 12
mos; those in the gingivitis group had significant mean PD reductions at 8 and 10 mos (p <
0.05). Compared with baseline CAL, gingivitis and periodontitis groups had significant gains
at 8, 10, and 12 mos; individuals in the healthy group had significant gains in CAL at 8 and
10 mos (p < 0.05). Significant radiographic bone gain was achieved in the mild periodontitis
group at 12 mos compared with baseline (p < 0.05). Compared with baseline, significant
percent reductions in the percent of sites with bleeding upon probing were seen in the gingi-
vitis and periodontitis groups at 8, 10, and 12 mos (p < 0.05). Significant reductions in the
percent of sites with plaque were observed in the periodontitis groups at 8, 10, and 12 mos
compared with baseline; healthy individuals and those in the gingivitis group had significant
reductions at 10 mos compared with baseline (p < 0.05). Significant reductions in the percent
of sites with redness were achieved by individuals in the periodontitis groups at 8 and 12 mos
compared with baseline; those in the healthy group had significant increases in the percent of
redness at 12 mos compared with baseline (p < 0.05).
756 Kinney et al. J Dent Res 90(6) 2011
Analysis of data from a cross-sectional study demonstrated
elevated concentrations of IL-1β and MMP-8 from whole saliva
of participants with periodontal disease compared with healthy
et al., 2007). Recently, Fine et al. longi-
tudinally evaluated PDP on children at
risk for aggressive periodontitis and
reported that IL-1β demonstrated a high
specificity and sensitivity to predict
alveolar bone loss (Fine et al., 2009).
Regarding biofilm pathogens, analy-
sis of our data revealed that periodontal
pathogens, specifically the “red com-
plex” pathogens (Socransky et al., 1998),
were able to predict PDP. Our findings
are supported by a recent report demon-
strating an association of periodontal
pathogens, inflammatory biomarkers,
and periodontal disease (Teles et al.,
2010). Results demonstrated a positive
correlation among mean levels of IL-1β,
IL-8, and MMP-8, and the proportions of
periodontal pathogenic bacteria in indi-
viduals with periodontitis (Teles et al.,
Although serum biomarkers have been
studied by several authors (Tonetti et al.,
2007; Renvert et al., 2009), our study
demonstrated that they did not appear
to be good predictors of PDP. Interest-
ingly, no significant changes on serum
biomarkers after non-surgical periodontal
treatment in pregnant women with
periodontitis were shown (Michalowicz
et al., 2009). Furthermore, it has also been
reported that analysis of serum biomark-
ers was inconsistent across individuals
and largely not sustainable (Behle et al.,
Our results support the concept of
combining clusters of salivary biomarkers
and periodontal pathogens for prediction
of future disease progression. The use of
panels of host biomarkers and pathogens
for disease diagnosis may hold promise
(Ramseier et al., 2009). Among the indi-
cators for PDP, the elevated presence of
“red complex” pathogens, F. nucleatum,
C. rectus, and P. intermedia, demonstrated
the ability to predict PDP for 82% of indi-
viduals. Salivary biomarkers, specifically
MMP-8, MMP-9, OPG, and IL-1β, pres-
ent in low concentrations were able to
predict stability for 78% of individuals
who were clinically stable during disease
monitoring. Interestingly, a selected group
of individuals was classified as indetermi-
nate regarding their clinical disease pro-
gression. A second cluster analysis within this specific group
showed that those undergoing clinical disease progression also
had high concentrations of periodontal pathogens. In addition,
Figure 3. Longitudinal plots of mean (± SD) salivary biomarker levels stratified by initial category
of periodontal health. Compared with baseline, significant reductions in salivary MMP-8 were
seen in the moderate/severe periodontitis group at 8, 10, and 12 mos; those in the healthy
group showed significant increases in MMP-8 levels at 12 mos (p < 0.05). Participants in the
moderate/severe periodontitis group had significant reductions in MMP-9 at 10 and 12 mos
compared with baseline; those in the healthy group had significant increases in MMP-9 at 10
and 12 mos compared with baseline (p < 0.05). Individuals in the periodontitis group had
significant reductions in salivary OPG levels at 12 mos compared with baseline (p < 0.05).
Compared with baseline levels of calprotectin, participants in the moderate/severe periodontitis
group showed significant increases at 8, 10, and 12 mos; those in the mild periodontitis group
had increases at 8 and 12 mos; those in the gingivitis group had increases at 8 and 10 mos;
and those in the healthy group had increases at 8, 10, and 12 mos (p < 0.05). Significant
increases in salivary ICTP were observed in the mild periodontitis and gingivitis groups at 12
mos compared with baseline (p < 0.05). Compared with baseline, significant decreases in IL-1β
levels were seen in the periodontitis groups at 8, 10, and 12 mos, in the gingivitis group at 8
and 10 mos, and in the healthy group at 10 mos (p < 0.05).
J Dent Res 90(6) 2011 Pathogen and Host-response Profiling for Periodontitis 757
those who demonstrated stability tended to have low levels of
salivary biomarkers, as did those initially considered stable.
Offenbacher et al. proposed a diagnostic periodontal disease clas-
sification scheme called the “biologic systems model” (Offenbacher
et al., 2007). This model is based on medical and dental findings
and contributory biologic phenotypes. Underlying “biologic phe-
notypes” consider the biofilm and the host inflammatory and
immune response to be at the biofilm-gingival interface. As a
whole, the biologic system model is built on a framework of com-
ponents, starting with the recognition of subject-level exposures
interacting with genetic and epigenetic factors, and including cel-
lular and molecular processes and inflammatory biomarkers to
define different clinical phenotypes of periodontal disease detec-
tion and prediction. Limitations of this investigation with the
sample evaluated include measurement error of PDP indices, the
lack of body mass index assessments, serum cotinine levels, and
analysis of the smoking contributions to biomarker assessments.
Future investigations in larger populations may provide greater
insights into these risk factors that may have confounded some of
the results in the study sample evaluated.
In summary, this investigation supports the use of microbial
and host-response biomarkers as indicators for periodontal dis-
ease progression. The use of saliva and biofilm biomarkers
offers potential for the prediction of periodontal disease progres-
sion or stability to potentially determine periodontal signatures
of disease in larger patient populations.
This work was supported by NIH (U01–DE014961), NCRR (UL
RR000042), and the Swiss Society of Periodontology. Drs. Herr,
Shelburne, Braun, Singh, and Giannobile hold intellectual property
related to this article. The authors appreciate the clinical assistance
of Amy Kim, Noah Smith, and Tina Huffman. We appreciate the
assistance of Mr. Chris Jung for the figures in the article.
Figure 4. Barplots displaying three clusters based on levels of salivary biomarkers, biofilm, serum biomarkers, and clinical measures. Within each
cluster, the number of participants undergoing disease progression (≥ 2 sites demonstrating > 2 mm of CAL loss over 6 mos) is indicated. Np =
number of participants within each group experiencing disease progression. Ns = number of participants within each group without disease
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