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Rethinking Periodontal Inflammation
Steven Offenbacher,* Silvana P. Barros,* and James D. Beck*
Clinical signs and symptoms, as well as medical and dental
history, are all considered in the clinical determination of gin-
gival inflammation and periodontal disease severity. However,
the ‘‘biologic systems model’’ highlights that the clinical pre-
sentation of periodontal disease is closely tied to the underly-
ing biologic phenotype. We propose that the determination
and integration of subject-level factors, microbial composi-
tion, systemic immune response, and gingival tissue inflam-
matory mediator responses will better reflect the biology of
the biofilm–gingival interface in a specific patient and may
provide insights on clinical management. Disease classifica-
tions and multivariable models further refine the biologic basis
for the increasing severity of periodontal disease expression.
As such, new classifications may better identify disease-
susceptible and treatment–non-responsive individuals than
current classifications that are heavily influenced by probing
and attachment level measurements alone. New data also
suggest that the clinical characteristics of some complex dis-
eases, such as periodontal disease, are influenced by the ge-
netic and epigenetic contributions to clinical phenotype.
Although the genetic basis for periodontal disease is consid-
ered imperative for setting an inflammatory capacity for an in-
dividual and, thus, a threshold for severity, there is evidence to
suggest an epigenetic component is involved as well. Many
factors long associated with periodontitis, including bacterial
accumulations, smoking, and diabetes, are known to produce
strong epigenetic changes in tissue behavior. We propose that
we are now able to rethink periodontal disease in terms of a bi-
ologic systems model that may help to establish more homo-
geneous diagnostic categories and can provide insight into the
expected response to treatment. J Periodontol 2008;79:1577-
1584.
KEY WORDS
Classification; epigenetics; periodontal disease; phenotype.
Standard periodontal assessment
has traditionally involved evaluat-
ing clinical signs and symptoms,
along with medical and dental history, to
determine the extent and severity of gin-
gival and periodontal tissue damage.
1,2
As such, the clinical presentation of dis-
ease, or clinical phenotype, is typically
based on clinical features of inflamma-
tion, such as redness, edema, and
bleeding on probing (BOP), and loss of
supporting tissue, as evidenced by prob-
ing depth (PD), attachment level, and
alveolar bone loss.
3
Despite the value of
these methods, such techniques often
result in inconsistent diagnoses as well
as an inability to reliably predict a
patient’s response to treatment.
4
A rea-
son for the limited success in predicting
the future course of disease in some
individuals is that clinical phenotype
does not reflect the underlying biologic
processes that occur at the biofilm–
gingival interface.
4
Baelum and Lopez
5
stated that ‘‘periodontal disease is a
syndrome that comes in all sizes,’’ sug-
gesting that there are no clear demar-
cations between health and disease or
between conditions. However, these in-
vestigators raised this hypothesis with-
out the burden of needing to explain
biologic data that indicated that there
are dramatic differences between indi-
viduals who may have very similar clin-
ical presentations or that patients with
identical clinical presentations may re-
spond differently to the same therapy.
Biologic processes, including the char-
acteristics of the biofilm and of the host
inflammatory and immune responses,
tend to vary among individuals, despite
* Center for Oral and Systemic Disease, Department of Periodontology, University of North
Carolina at Chapel Hill, Chapel Hill, NC.
doi: 10.1902/jop.2008.080220
J Periodontol • August 2008 (Suppl.)
1577
producing a similar clinical picture or diagnostic cat-
egory.
4
As such, disease classification should be
based on clinically apparent signs and symptoms
and reflect the underlying biologic phenotypes that
contribute to pathogenesis in a given individual at
the microbial, cellular, and molecular level.
4,6
Other
factors that may influence the biologic phenotype
and clinical expression of disease include interindivid-
ual variability that exists at the subject-level, such as
unique environmental exposures, as well as differ-
ences in genetic and possibly epigenetic composi-
tion.
6-8
By delineating the multiple factors that
contribute to the clinical presentation of periodontal
disease in a specific individual, it should be possible
to develop more homogeneous diagnostic categories
for periodontitis in which to place individuals at risk.
This should allow a more accurate prognosis and pro-
vide insight into the ideal customized treatment for a
given individual.
DISEASE CLASSIFICATION BASED ON
COMBINATIONS OF CLINICAL AND
BIOLOGIC CHARACTERISTICS
It is now more widely appreciated that any clinical
phenotype can be broken down into respective sub-
clinical components to provide a model for classifying
disease in humans.
9
Such a ‘‘biologic systems model’’
provides a critical framework with which to view the
components of disease. In periodontal disease (Fig.
1),
4
the biologic systems model would incorporate
all components that contribute to the final clinical
phenotype (i.e., the clinical presentation of disease).
The outermost component would be the unique expo-
sures of the individual at a subject level, including the
characteristics of the biofilm (e.g., the type of bacte-
rial infection) as well as the presence of other health
conditions (e.g., diabetes and obesity) and environ-
mental influences (e.g., toxins within cigarette
smoke). The subject-level component interacts with
the genetic background. For example, the evidence
suggests that specific gene polymorphisms are asso-
ciated with periodontal disease,
10
some of which
seem to influence the host’s response to infections.
We are just starting to investigate the epigenetic alter-
ations of the host that potentially shape the biologic
phenotype. The biologic phenotype includes the cel-
lular and molecular processes that often include in-
flammatory biomarkers that are associated with
clinical phenotype.
4
With multiple components con-
tributing to the clinical phenotype of periodontal dis-
ease, the model clearly shows how individuals with the
same clinical presentation may have very different
predisposing factors. By categorizing clinical pheno-
type based on the severity of periodontal disease and
deducing the common predisposing factors that con-
tribute to these categories, we can hypothesize that
there are different biologic factors underlying peri-
odontal disease in different individuals.
CLINICAL DISEASE CATEGORIES DIFFER
IN BIOFILM CHARACTERISTICS AND
BIOLOGIC PHENOTYPE
To help resolve the subject-level factors and biologic
phenotype associated with the increasing severity of
periodontal disease, a recent epidemiologic study
4
was undertaken involving >6,700 older adults (aver-
age age, 62 years) exhibiting some degree of attach-
ment loss. Clinical characteristics of periodontal
disease were collected through cross-sectional as-
sessment of such features as attachment level, plaque
index, BOP, gingival index, and PD (as measured at
six sites per tooth on all teeth; expressed as extent
scores). When we analyzed markers of inflammatory,
microbial, and immune response characteristics, sub-
jects predominantly clustered upon the clinical signs
that reflected a combination of PD
11
and BOP.
4
Such
categorization was supported by multivariate models
in which extent BOP and PD scores were associated
with distinct inflammatory, immune, and microbial
characteristics that resulted in separate biologic phe-
notypes. Thus, although the biologic markers were
used to define clusters of biologic phenotypes, some
clinical parameters could be applied to partition these
groups, albeit with some overlap and heterogeneity.
Accordingly, these distinct biologic phenotypes could
be grouped into five conditions ranging from the
healthiest to most diseased state, based on shallow
Figure 1.
Proposed biologic systems approach to defining periodontal disease
at the biofilm–gingival interface. Modified from reference 4.
Rethinking Periodontal Inflammation Volume 79 • Number 8 (Suppl.)
1578
or deep PDs in combination with low or high bleeding
scores.
12
Based on feedback, we decided to describe
these biologic phenotypes as conditions using clinical
signs that delineate these conditions and use termi-
nology that is consistent with a clinically defined clas-
sification rather than describing these biologic
phenotypes using their marker traits. Thus, we cre-
ated the following conditions, prefacing them with
the term biofilm–gingival interface (BGI), to indicate
that they are not equivalent to traditional disease
classifications:
dBGI-H: biofilm–gingival interface-healthy (PD £3 mm,
BOP extent scores <10%).
dBGI-G: BGI-gingivitis (PD £3 mm, BOP extent
scores >10%).
dP1: BGI-deep lesion/low bleeding (PD ‡4 mm, BOP
extent scores <10%).
dP2: BGI-deep lesion/moderate bleeding (PD ‡4
mm, BOP extent scores 10% to 50%).
dP3: BGI-deep lesion/severe bleeding (PD ‡4 mm,
BOP extent scores ‡50%).
Although the majority of individuals in the study pop-
ulation fell within the P2 category (39.7%), almost equal,
but lower, proportions of remaining individuals fell within
categories of BGI-H, BGI-G, P1, and P3 (14.3%, 15.1%,
18.0%, and 12.9%, respectively).
Biologic Phenotypes Vary Across Gradient Levels
of Disease
Inflammatory, immunologic, and microbial composi-
tions at the BGI were assessed in detail to determine
the biologic phenotype groupings. These were ranked
and split into the various categories of disease severity
as described above.
4
Although these categories re-
flect a gradient in disease severity because they differ
in underlying biologic characteristics, they should not
be viewed as a continuum. Rather, it seems more
likely that these represent different biologic condi-
tions of the periodontium that have different clinical
presentations. Clearly, there is overlap in the shared
biologic processes between categories, as well as cer-
tain subjects with biologic phenotypes who possess
clinical signs that might misclassify to a degree; how-
ever, these are still independently robust disease
groupings. Multivariate analyses were performed in
which microbial counts, serum immunoglobulin G
(IgG) levels, and gingival crevicular fluid (GCF) bio-
markers were assessed separately, and their associa-
tion with various categories of disease severity was
determined.
To define the microbial species within the peri-
odontal pocket that make the greatest contribution to
the increasing severity of disease in the model, DNA
checkerboard methods were used in addition to the
more traditional measure of plaque index. Microbial
load, in terms of total subgingival biofilm counts, varied
among clinical categories, with total biofilm counts
increasing from healthy tissues to gingivitis tissues
(BGI-G) and from the least to most severe categories
of periodontal disease (P1–P3). Furthermore, the host
immune response to the biofilm provides evidence as
to which organisms within the biofilm are activating
an inflammatory immune response. However, perhaps
more relevant to defining the underlying biologic phe-
notype are the particular microbial species that dis-
criminate between each category of disease. When
species most indicative of disease were considered in-
dependently or as components of the ‘‘red’’ type (e.g.,
Porphyromonas gingivalis,Tannerella forsythia [previ-
ously T. forsythensis], and Treponema denticola)or
the ‘‘orange’’ type (e.g., Prevotella intermedia,Prevo-
tella nigrescens,Campylobacter rectus,andFusobacte-
rium nucleatum) microbial complexes,
13
significant
differences were observed between clinical categories
of disease in terms of IgG response and microbial
burden. Figure 2 shows the serum IgG response to
the total biofilm, the orange and red complexes, and
specific bacteria within the biofilm for each of the
BGI disease groups compared to the BGI-H group.
Serum antibody concentrations to various mi-
crobes were determined with immunocheckerboard
methods and are supported by trends also observed
with microbial counts (Fig. 3). As shown in Figure
2, antibody titers to C. rectus were the most closely
associated with BGI-G, with a 1.3-fold increase in
biofilm-specific mean serum IgG levels compared to
healthy tissues. In periodontal disease groups, ele-
vated titers against P. gingivalis were the most
strongly associated with severe periodontitis (3.1-fold
increase in P3 compared to BGI-H), as were titers
against C. rectus (1.8-fold increase between P3 and
BGI-H). Using highest likelihood score model proce-
dures, IgG titers to C. rectus for BGI-G and P. gingivalis
for P1 through P3 were the strongest two predictor titers.
Figure 3 illustrates the unadjusted fold change in
mean bacterial counts as determined by DNA check-
erboard analysis among BGI disease categories rela-
tive to BGI-H. Counts for microbes within the ‘‘orange’’
complex seemed to be most strongly associated with
increasing gradient levels of disease within the cate-
gories describing the transition from healthy tissues
to gingivitis tissues (i.e., BGI-H to BGI-G), as well as
with a greater severity of periodontitis (P1 through
P3). The total microbial counts, as well as counts
within the orange and red complexes, were lower in
P1 than in gingival BGI-G or BGI-H. These findings re-
flected the lower plaque scores and were consistent
with the greater tendency for the BGI-P1 disease
group to have lower BOP scores. Of all the microbial
species that increase with disease, the single stron-
gest association with increasing disease severity, after
J Periodontol • August 2008 (Suppl.) Offenbacher, Barros, Beck
1579
adjusting for plaque- and subject-level variables, was
C. rectus, which was significantly two-fold higher in in-
dividuals with inflamed gingival tissues (BGI-G) com-
pared to those with healthy tissues (BGI-H). Counts
for C. rectus were also higher in the periodontitis
groups P3 and P2 compared to P1 (3.3- and 2.0-fold
increases, respectively). Microbial counts for P. gingi-
valis were also the single best predictor associated
with advanced periodontal disease (using highest
likelihood score model analyses in full multivariate
models), with higher counts in P3 compared to P2
and P1 (1.8-fold versus BGI-H).
Multivariate logistic analysis confirmed that the
plaque index remained significant across all groups
compared to healthy tissues, despite incorporating
microbial counts of the various species into the model.
That is, even including measurements of these major
periodontal pathogens in the models, we could not ex-
plain all of the influences of plaque on disease expres-
sion. These findings suggest the need for further
characterization of the flora in the biofilm, in that there
are likely unknown, perhaps uncultivable, organisms
not detected by the method of checkerboard analysis
used in the study that contribute to disease. However,
with antimicrobial antibody methods demonstrating a
similar trend in microbial species associated with the
increasing severity of periodontal disease indicating
exposure of the host immune system
to the microorganisms, it was con-
firmed that C. rectus is involved in
the infection in gingivitis and that C.
rectus and P. gingivalis are critical
in BGI-P1, P2, and P3.
Analysis of the inflammatory me-
diators that are present within distinct
categories of disease demonstrated
that IL-1bin gingival crevicular fluid
seems to play a predominant role in
defining the different disease states.
An increase in IL-1blevels was de-
tected across all disease groups
(2.0-fold increase in P3 and a 1.7-fold
increase in P2 compared to BGI-H). In-
flammatory mediators, monocyte che-
motactic protein (MCP)-1, regulated
upon activation, normal T-cell ex-
pressed, and secreted (RANTES),
and IL-6, may also have contributed
to differences in disease expression
observed in the most severe disease
(P3) compared to BGI-H, being ele-
vated 4.5-, 18.8-, and 6.2-fold, re-
spectively (data not shown here).
Individuals with the most severe form
of periodontal disease (P3) had a qual-
itatively and quantitatively different
inflammatory response compared to the other peri-
odontal disease groups (P2 and P1), demonstrating el-
evations in IL-1band prostaglandin E
2
. Increased IL-6
and MCP-1 are associated with the most advanced
forms of periodontitis, consistent with the role these
molecules play in mediating chronic inflammation.
These findings in toto suggest that these classifica-
tions based upon PDs and BOP scores may provide
important insight into the biologic phenotypes that
may better direct diagnoses and treatments. For ex-
ample, it is significant that many subjects within the
P1 group would be classified as having the most ad-
vanced or severe periodontal disease using traditional
classifications that are based upon PDs and attach-
ment loss measurements. Many of these subjects
might readily meet eligibility requirements for en-
trance into a clinical trial designed to treat periodontal
disease and traditionally would have been grouped
with P2 and P3 subjects. However, their biologic phe-
notype differs, and they may represent a different peri-
odontal condition that should be considered separately.
For example, within P1 subjects, 39.7% of teeth have
‡3 mm attachment loss, and 12.1% of these subjects
meet the Centers for Disease Control and Prevention
criterion for severe disease. Furthermore, P1 subjects
have an average of 6.5 interproximal sites with PD
‡5 mm plus attachment loss ‡3 mm at that site.
4
Figure 2.
Serum IgG response to the total biofilm, the ‘‘orange’’ and ‘‘red’’ complexes, and specific
bacteria within the biofilm for each of the BGI disease groups compared to the BGI-H.
A. actino.=A. actinomycetemcomitans. Data from Offenbacher et al.
4
Rethinking Periodontal Inflammation Volume 79 • Number 8 (Suppl.)
1580
However, the GCF inflammatory mediator level and
microbial load within the P1 group are actually more
consistent with the BGI-H group.
4
These subjects
have much higher ‘‘resting’’ titers against oral orga-
nisms in the serum coupled with lower subgingival
counts. Potentially, many clinicians might suspect
that these subjects are relatively clinically stable
and may not respond to therapy—principally based
upon the low bleeding scores. These findings suggest
that one might not expect these subjects to be very re-
sponsive to additional therapy, especially antimicro-
bial-based therapy, despite the presence of several
diseased periodontal sites.
In a similar manner, these findings suggested that
shallow pockets with gingival inflammation, such
as that exhibited by the BGI-G group, may not be as
innocuous as we previously believed because the
subgingival microbial environment has an emergence
of C. rectus, a strong predictor of the most severe
BGI-P3 classification as well as ‘‘disease-aggressive’’
elevations in proinflammatory mediators. Because
the cellular and molecular processes ultimately
mediate disease progression, these findings sug-
gested that this form of clinical disease may be
more worthy of our clinical attention than previously
appreciated.
Subject-Level Factors That
Contribute to the Gradient
Levels of Disease
In addition to biologic phenotype,
there are important subject-level var-
iables that shape the host response to
an organism and, thus, may contrib-
ute to the clinical phenotype.
6
These
subject-level factors are well defined
in terms of the increased risk for pro-
gression of gingivitis to advanced
stages of periodontal disease and in-
clude smoking, obesity, and diabetes,
as well as other exposures.
14,15
For
example, although smoking may con-
tribute to worsening clinical severity
through toxins released within the oral
cavity or via oxidative stress, diabetes
may increase the risk for periodontal
disease through enhanced inflamma-
tory responses and depressed wound
healing.
7
The unique aspect of the in-
fecting organisms within the biofilm is
also a subject-level factor, with only a
relatively small proportion of all bacte-
rial species consistently found within
diseased tissue sites.
7
After entering subject-level varia-
bles into a logistic model with respect
to categories defining gradient levels
of disease, some interesting trends emerged.
4
Signifi-
cant differences were found for race, gender, diabetes,
education, smoking, and body mass index among cat-
egories of increasing severity of periodontal disease.
Of all the subject-level factors, race, gender, and the
presence of diabetes were the strongest contributors
to disease expression. As such, findings confirmed that
predisposition based on individual exposures were
critical to how the disease is expressed in the model.
GENETIC AND EPIGENETIC MODULATION
OF BIOLOGIC PHENOTYPE IN
PERIODONTAL DISEASE
Increasing evidence suggests that subject-level varia-
bles interact with an individual’s genetic and epige-
netic composition to influence biologic phenotype
and ultimately disease expression. As such, variabil-
ity in genotype and epigenotype between individuals
is suggested to contribute to the diverse range of re-
sponses in inflammatory reaction observed at the mo-
lecular and cellular level.
That genotype is important in determining predis-
position as well as clinical presentation of periodontal
disease is well documented on the basis of twin stud-
ies.
16-19
A greater variability in the risk for periodontal
disease was observed between sets of dizygotic twins
Figure 3.
Unadjusted fold change in bacterial counts as determined by DNA checkerboard analysis
among BGI disease categories relative to BGI-H. A. actino.=A. actinomycetemcomitans.
Data from Offenbacher et al.
4
J Periodontol • August 2008 (Suppl.) Offenbacher, Barros, Beck
1581
compared to monozygotic twins, with the underlying
assumption that environmental exposures within
families are similar.
16,17
Thus, the genetic component,
differing only in dizygotic twins rather than in identical
twins, seems to play a large role in determining the pre-
disposition to periodontitis. There is also substantial
evidence for genetic differences in the exuberance of
the inflammatory response based on the levels of ex-
pression of inflammatory mediators, such as IL-1
and tumor necrosis factor-alpha, which are associated
with the increased risk for chronic or aggressive peri-
odontitis.
7
Although a number of polymorphisms that
seem to increase the risk for periodontal disease have
been determined, it is anticipated that many more, as
well as their specific interaction with environmental ex-
posures, will be found.
7
For example, it was recently
demonstrated by Rogus et al.
20
that certain haplotype
pairs of IL1B were associated with higher (28% to 52%)
local levels of GCF IL-1b, after adjusting for the level of
periodontal disease. Thus, there are clearly genetic de-
terminants of the inflammatory response that modu-
late the expression of disease.
Although genetic composition influences the bio-
logic response by setting an inflammatory capacity
for an individual, there is increasing evidence that
epigenetics is critical for regulating the inflammatory
response in a dynamic way.
21
Rather than involving
variability of the genetic sequence itself, epigenetic reg-
ulation is a reversible modification in gene expression
that is determined by environmental exposures and
may be heritable.
22
As such, a range of responsiveness
is possible that differs from that imparted at birth
through genotype and may be particularly vulnerable
to alteration through subject-level exposures. It was
shown that even when a genotype is the same at a spe-
cific locus comparing two individuals, one may differ
from the other due to epigenetic modifications induced
by differences in exposures.
8
There are two major mechanisms by which epige-
netic alterations are known to occur.
22
The first is
methylation of cytosine residues at 59-Cystosine-
phospho-Guanosine-39(CpG) sites in DNA, particu-
larly of CpG-rich islands. Under normal physiologic
conditions, methylation of CpG sites is maintained
by DNA methyltransferases as a regulatory mecha-
nism for silencing specific genes, as well as a protective
mechanism for defense against viral sequences.
22
However, CpG islands, which are (G+C)-rich regions,
are disproportionately present within promoter regions
and are generally maintained free of methylation.
22
Methylation of these CpG-rich stretches directly in-
hibits binding of transcriptional factors to the site, re-
sulting in gene silencing.
22
Aberrant methylation at
these sites is associated with the development of sev-
eral forms of human cancers, such as throughthe focal
silencing of tumor suppressor genes.
23,24
However,
hypomethylation of certain genes is also associated
with early events in tumorigenesis.
25
The second major mechanism underlying epige-
netic regulation of DNA involves modifications of his-
tone proteins and is linked, at least in part, to the DNA
methylation process. Along with the direct action of
DNA methylation on inhibiting transcription, methyl-
ation of DNA acts through recruitment of methyl-
CpG–binding proteins and histone deacetylase that
can interact with histones to change chromatin con-
formation.
25,26
In general, although acetylation of
lysine residues on the amino-terminus of histones ac-
tivates gene expression, hypoacetylation is an indica-
tor of transcriptionally quiescent genes.
22
Direct
methylation of histones can result in gene silencing
or activation, depending on which lysine residue is
methylated.
22
Epigenetic modification through acet-
ylation or methylation of histone proteins is associ-
ated with alterations in chromatin conformation that
induce changes in transcription of DNA and corre-
sponding physiologic activity.
26
Some epigenetic changes may be passed from a
parental chromosome in the gamete or zygote to alter
gene expression of the somatic cells of the off-
spring.
25,27
Such changes are influenced by family
history, as well as environmental exposures (e.g., in
utero nutrition and toxins), and may have genome-
wide (global) effects in all cells or only in specific tis-
sues or during certain developmental stages.
25,28
However, more dynamic changes in epigenetic mod-
ulation may occur throughout the life of an individual,
conferring phenotypic plasticity in response to such
factors as diet, aging, and toxins.
28
Alterations in
global epigenetic profiles, either inherited or acquired
somatically, are associated with certain disease states.
22
In addition, cigarette smoke is well known to induce
global hypomethylation and is associated with the de-
velopment of squamous cell head and neck cancer.
29
Diabetes is another area to which epigenetic modula-
tion has been linked.
30
These types of epigenetic al-
terations tend to persist following cell division and,
therefore, can impart transmissible, epigenetically mod-
ified phenotypes onto subsequent cells of the lineage.
A clear association between microbial infection
and epigenetic modification has been found between
mucosal bacterial infection and epigenetic alterations
associated with the increased risk for gastric cancer,
31
possibly reflecting the proposed evolutionary ancient
role of DNA methylation as a mechanism for shutting
down gene sequences to protect the host from inva-
sion by foreign DNA (e.g., parasitic elements).
25,32
However, a detrimental effect to the host from an al-
tered epigenotype is also apparent, with epigenetic
changes associated with a number of diseases and de-
velopmental abnormalities, including chronic condi-
tions like atherosclerosis.
21,25
Rethinking Periodontal Inflammation Volume 79 • Number 8 (Suppl.)
1582
Oral infection may also lead to epigenetic altera-
tions, locally within gingival tissue or more globally
with widespread effects. Technology is available to
determine genomic-wide epigenetic changes
26
that
can be applied to study tissues at the biofilm interface
to compare diseased tissue to healthy control tissue.
Such microarray technology
33
may prove successful
for identifying a number of hypermethylated and hy-
pomethylated genes associated with periodontal in-
fection (data not shown). For example, preliminary
findings suggested that the gene for IL-6, encoding
a cytokine involved in the final differentiation of B cells
into immunoglobulin-secreting cells, undergoes a de-
crease in methylation (hypomethylation) in periodon-
tal disease tissues compared to control samples
(unpublished data). These preliminary findings sug-
gested that the IL-6 gene may be preferentially acti-
vated or upregulated in expression in periodontal
disease compared to healthy control tissues, which
is consistent with observed increases in GCF IL-6
levels.
4
With the possibility of differential methylation
patterns of many candidate genes remaining to be
identified and confirmed, there is great potential for
elucidating a variety of other genes whose expression
may contribute to periodontal disease. The possibility
that differential methylation induces changes in the
host genome that may contribute to the diseased state
provides an additional avenue for research into the bi-
ologic phenotype underlying the clinical presentation
of periodontitis.
CONCLUSIONS
Clinical signs and symptoms along with medical his-
tory generally form the basis for establishing the diag-
nosis and assessing the severity of periodontal
disease. However, the ‘‘biologic systems model’’ ap-
proach argues for elucidating subject-level exposures,
including the genetic and epigenetic components, as
well as establishing the biologic phenotype to deter-
mine the clinical phenotype of periodontal disease.
A number of subject-level variables, as well as inflam-
matory markers and other indicators at the cellular
and molecular levels, were used to develop a new hy-
pothetical classification of periodontal disease with
five apparently distinct categories. These categories
represent the transition from health to increasing se-
verity of disease, at least in the older population stud-
ied, and include classification of the healthy and
gingivitis stages, as well as three discrete categories
of periodontal disease severity. Prospective studies
are warranted to establish that this classification sys-
tem, based on cross-sectional data, is valid in a
broader subject population and involving a wider
range of periodontal conditions. A key limitation of
these data is that these classifications are based upon
an older dentate population with a mean age of ;62
years. Subjects with rapidly progressive or early-
onset forms of disease are probably not represented.
In addition, it is essential to determine whether the cat-
egorization is clinically meaningful, i.e., does it enable
us to better customize treatment and improve treat-
ment outcomes? Theoretically, understanding the bi-
ologic bases underlying different clinical phenotypes
of periodontal disease should aid us in establishing
prognosis and developing novel and more personal-
ized treatments.
ACKNOWLEDGMENTS
The authors acknowledge the support of grant
M01-RR00046 from the National Center for Research
Resources, National Institutes of Health, Bethesda,
Maryland. The initial draft of this manuscript was de-
veloped by a medical writer (Axon Medical Communi-
cations Group, Toronto, Ontario) based on content
provided solely by the authors. The final manuscript
submitted was under the sole control of the authors.
There are no conflicts of interest for any of these inves-
tigators regarding the results presented.
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Correspondence: Dr. Steven Offenbacher, Department of
Periodontology, School of Dentistry, University of North
Carolina at Chapel Hill, Manning Drive and Columbia
Street, CB #7450, Chapel Hill, NC 27599. E-mail:
steve_offenbacher@dentistry.unc.edu.
Submitted April 24, 2008; accepted for publication May
30, 2008.
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