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Jaedicke K, Preshaw PM, Taylor JJ. Salivary cytokines as biomarkers of

periodontal diseases. Periodontology 2000 2016, 70(1), 164-183.

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This is the peer reviewed version of the following article: Jaedicke K, Preshaw PM, Taylor JJ. Salivary

cytokines as biomarkers of periodontal diseases. Periodontology 2000 2016, 70(1), 164-183. which has

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Salivary cytokines as biomarkers of periodontal diseases

Katrin M. Jaedicke, Philip M. Preshaw & John J. Taylor

John J Taylor PhD

Centre for Oral Health Research & Institute of Cellular Medicine Level 7 School of Dental Sciences Newcastle University Framlington Place NE2 4BW UK Tel: +44(0)191 2088694 (Office) Tel: +44(0)191 208 6137 (Fax) e-mail: [email protected] Running title: Salivary cytokine biomarkers Monograph title: Gingival crevicular fluid and Saliva

(Guest Editors: John J. Taylor & Philip M. Preshaw)

mailto:[email protected]

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Abstract

Research into biomarkers of periodontitis is driven by mainly three targets: to identify “at

risk” patients before periodontal tissue destruction occurs, to determine disease activity

and progression, and to build up our understanding of this complex disease with the

purpose of finding new therapeutic targets. Whilst previously blood and gingival crevicular

fluid were the biological samples of choice, recently saliva has gained more attention as a

readily accessible oral fluid and which has a mediator profile similar to that of serum and

gingival crevicular fluid. The aim of this paper is to give a comprehensive overview of

salivary cytokines in periodontitis, highlighting extensively studied cytokines such as

interleukin-1β and interleukin-6, but also cytokines which only have been the subject of few

studies and which warrant further investigation. Cross-sectional and longitudinal studies of

salivary cytokines and the potential of cytokines as periodontitis biomarkers are evaluated.

Finally, a discussion of potential confounding factors such as concurrent systemic diseases

and smoking is presented.

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Periodontal disease is time consuming and expensive to treat and therefore its prevention,

early detection and management are issues which, if effectively addressed, are likely to yield

considerable healthcare benefit [97]. However, despite numerous advances in our

understanding of the pathogenesis of chronic inflammatory diseases, periodontitis is still

only diagnosed once connective tissue and bone destruction has occurred. Furthermore,

monitoring disease progression is a highly skilled and technically demanding process

involving measurement of bleeding on probing, probing depth and attachment loss coupled

with radiographic assessment and (subjective) visual observations [76]. The development of

biomarkers for early detection of periodontal disease and to identify progression would be

highly desirable as current diagnostic approaches do not reflect current disease activity, but

simply assess the cumulative effects of historical tissue destruction [53]. Rational diagnosis

would also have concomitant patient benefit as the paucity of evidence-based knowledge of

disease progression in individual patients may lead to unintentional clinical mismanagement

[97]. In addition, studies of the salivary mediators associated with disease may help in the

development of novel therapies aimed at controlling cytokine bioavailability (e.g. through

anti-cytokine antibodies, antagonists or soluble receptors) or targeting the intra-cellular

signalling pathways they activate, approaches which have been successful in the treatment

of other chronic inflammatory diseases such as rheumatoid arthritis [60, 91, 95].

Cytokines have been defined as soluble factors produced by one (immune) cell that

act on another cell within the same milieu [26] . However, it is now recognised that the

range of molecules with cytokine-like activity can be extended to include, for example

growth factors and adipokines which also have immunoregulatory functions. Importantly,

cytokine functions often overlap or merge, building a complex immunoregulatory network

in the immune system that is often perturbed in disease. It is increasingly appreciated that

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cytokines have vital roles in the development and homeostasis of numerous cell types and,

in a wide range of tissues, have roles in resolution of inflammation, wound healing, repair

and regeneration. In the following review, the term “cytokine” will be used in this broad

context. In addition to direct analysis of cytokines, the levels of molecules such as matrix

metalloproteinases and tissue inhibitors of metalloproteinases which are regulated by

cytokines have also been given considerable attention as potential periodontitis biomarkers,

as reviewed elsewhere in this issue.

Cytokines and periodontal diseases

It is now widely accepted that although the initiating factors in gingivitis and periodontitis

are the microbial elements of the dental plaque biofilm, the pathogenesis and concomitant

tissue destruction is driven by the development of a chronic, inflammatory host immune

response, the nature and extent of which are fundamental determinants of susceptibility

and progression. The function of immune cells and periodontal tissue cells in orchestrating

periodontal disease pathogenesis is underpinned by the functional diversity of cytokines

[56, 79]. The earliest cytokine studies in periodontal research can be dated back to the

1980s [20, 62] and coincide with the detailed characterisation of interleukin-1, interleukin-2

and tumor necrosis factor- [26]. These experiments showed increased thymocyte

proliferation induced by gingival crevicular fluid from inflamed sites compared to non-

inflamed sites, which was presumed to be due to cytokine-like activity, although the precise

molecular nature of the mediators remained to be determined [20, 62]. With the

establishment of enzyme-linked immunosorbant assays, interleukin-1 β was the first

cytokine to be specifically measured in the gingival tissue of patients with periodontal

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disease [44]; since then, many publications have reported the measurement of cytokines in

periodontal tissues, periodontal cell culture supernatants and oral fluids [17, 56, 79, 102].

While early studies focused on serum and gingival crevicular fluid samples for

investigations of periodontitis-related cytokines, saliva has come into focus as an alternative

source of biomarkers in recent years [17, 102, 115]. Saliva is made up of the exocrine

secretions of the salivary glands in addition to gingival crevicular fluid, microbial

components and food remnants [10, 38]. Analysis of saliva, similar to gingival crevicular

fluid, gives a better representation of the local pathological changes in the mouth than

analysis of serum. However, saliva has several advantages over gingival crevicular fluid, as it

is more easily accessible than gingival crevicular fluid, can be sampled in a much larger

volume without the need for clinical facilities and there are no complex skills necessary for

saliva sampling. Furthermore, whereas gingival crevicular fluid content reflects

inflammatory processes at individual disease sites, it is reasonable to suggest that saliva

content reflects a consensus ‘whole of mouth’ inflammatory status, which is likely to be

much more clinically relevant.

The “Biomarkers Definitions Working Group” defines a biomarker as: “A characteristic that

is objectively measured and evaluated as an indicator of normal biological processes,

pathogenic processes, or pharmacologic responses to a therapeutic intervention” [12].

Individual biomarkers should be discriminatory and robust (see below). Also, the utility of a

biomarker needs to be assessed using appropriate study design and methodology [57]. Most

studies are simply designed to be cross-sectional, comparing the levels of cytokines in

groups of healthy volunteers and periodontal disease patients. Superficially, such studies

merely report associations of biomarkers with the presence of disease and therefore test

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whether or not a particular marker is discriminatory. Many, but not by any means all,

studies attempt statistical correlation with clinical parameters of periodontitis such as

clinical attachment loss and probing depth; this approach is weakened by the knowledge

that these parameters may merely reflect historical disease activity rather than

contemporary processes. Cross-sectional studies do not indicate whether or not a particular

salivary biomarker mirrors disease progression and/or response to treatment. Studies of

longitudinal changes in salivary biomarkers to date have been rather limited and have used

highly variable clinical protocols [17, 102]. Although these studies help identify candidate

disease markers and many have replicated data derived from studies of gingival crevicular

fluid, the majority of studies relate to a limited number of popular biomarkers and a

significant number of studies have not been reproduced independently, at least using

comparable methodologies [102]. It has also been noted that many clinical research studies

of periodontal disease fail to report examiner calibrating procedures for the measurement

of parameters such as probing depth, attachment loss or bleeding on probing [42]. The lack

of uniformity in periodontitis definitions is another factor that makes comparisons of

different studies challenging [78]. Another potential flaw in study design is the quality of the

methodology to assess cytokine concentrations in saliva. Enzyme-linked immunosorbant

assays clearly have the advantage in terms of high throughput and high reproducibility.

Most research studies use commercially available enzyme-linked immunosorbant assays kits

for sample analysis, however the efficacy of these kits in measuring mediators in saliva has

not been reported in any study; most assays have been optimised by the manufacturers for

use with serum samples. Failing to validate an enzyme-linked immunosorbant assay for its

suitability for use with saliva samples can lead to erroneous results and could partly explain

inconsistencies in findings between different studies; for example we have noted same

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assays exhibit poor sample recovery from saliva and poor inter-assay variation for

measurement of salivary mediators [47]. Similar data have been reported in a study

comparing the performance of multiplex cytokines assays although recovery can, in some

cases, be improved by sample pre-treatment [15].

Concerns regarding the widespread adoption of inappropriate statistical approaches

in biomedical research have been raised in the literature [30, 72]. Our review of the

literature on cytokine biomarkers for periodontal disease (see tables) revealed that whilst

the majority of studies apply correct statistical analysis procedures, there remain not

infrequent examples of statistical errors: e.g. the use of parametric tests (e.g. t-tests) when

data are not parametric; the use of dependent samples tests when data are independent

(for example paired samples t-tests instead of independent samples t-test); inappropriately

using a mixture of parametric and non-parametric tests (for example, performing an analysis

of variance followed by Mann-Whitney-U comparisons). In addition, few studies report a

formal power calculation for sample size and likely many of the reviewed studies are

underpowered. Furthermore, although most studies did take normal distribution of their

sample groups into account, many used the Kolmogorov–Smirnov test to assess normal

distribution and only a minority of studies used the Shapiro-Wilk test. This is of concern as

the Kolmogorov–Smirnov test is valid for a sample sizes of n>2000 whereas the Shapiro-Wilk

test of normal distributions is valid for a smaller sample sizes and has superior statistical

power [83]. Some studies have reported data from receiver-operator characteristic analysis

[29, 80] which establishes the efficacy of biomarker analysis in identifying individuals in an

unselected population sample with or without a particular condition such as periodontitis by

comparing sensitivity and specificity of a particular technique [112]. The value of receiver-

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operator characteristic statistics clearly depends on the application of the biomarker

analysis: it has value to confirm the robustness of an approach to identifying disease (e.g.

for use by untrained workers in the field) but does not indicate whether the test outcome is

relevant for disease progression which is of greater interest in disease management. Clearly,

much remains to be done to improve study designs, analysis, and reporting in studies of

biomarkers levels in periodontal diseases.

Salivary cytokines in oral health and periodontal disease

Interleukin-1 family

The interleukin-1 family of cytokines is defined on the basis of structural similarity and

comprises 11 members, further divided into 3 sub-families: the interleukin-1 sub-family

(interleukin-1, interleukin-1 interleukin ),

the interleukin-18 sub-family (interleukin-18, interleukin-37) and the interleukin-36 sub-

family (interleukin-36, interleukin-36 interleukin interleukin

interleukin-36 receptor antagonist) [36, 109]. The interleukin-1 cytokines are produced by a

wide variety of cell types and perform critical functions in innate and adaptive immune

responses to infection [36, 109]. In terms of pathogenesis, the interleukin-1 cytokines are

generally pro-inflammatory but and interleukin-36Ra have

antagonistic functions [36, 109].

Interleukin-1β is the prototypical interleukin-1 cytokine and as such is the most

extensively studied cytokine in clinical studies of salivary biomarker in periodontal disease.

Interleukin-1β has a multitude of immunological functions including activation of

neutrophils, T-and B-cells during infection as well as mediating liver acute phase protein

release and febrile responses (reviewed in [27]). In periodontitis, interleukin 1β is associated

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with neutrophil recruitment and activation of osteoclasts through its ability to induce

chemokines and activate osteoclasts [79]. Furthermore, the association between

periodontitis and elevated interleukin-1β in gingival crevicular fluid is well established [7,

17]. In case control studies, 9 out of the 12 studies show a significantly higher interleukin-

1β level in periodontitis compared to healthy controls (Table 1). In other studies (n=3),

interleukin-1β levels are significantly correlated with clinical parameters of periodontitis

(Table 1). In addition, there are consistent findings from 3 independent studies that levels of

salivary interleukin-1β decrease after periodontal treatment (Table 1). Interleukin-1β

concentrations in saliva are not affected by systemic conditions such as diabetes or smoking

(see below). The potential of salivary interleukin-1β to discriminate between periodontal

health and disease with high specificity and sensitivity in receiver operator curve analysis

has been investigated but with conflicting results [29, 80]. It is noteworthy that there is

considerable variation in the levels of salivary interleukin-1β reported; levels in health range

from an average of 7.24 ± 7.69 pg/ml [29] to as much as 633.91 ± 91.0 pg/ml [103] and

levels in periodontitis patients range from an average of 90.94 ± 85.22 pg/ml [29] to 1312 ±

691.22 pg/ml [50]. Notwithstanding differences in patient selection criteria, these findings

make precise comparisons between studies challenging. Thus, it would seem there is

substantial but not unanimous support for the notion that salivary interleukin-1β is a robust

choice of analyte for studies of periodontal disease biomarkers.

Only a limited number of studies have investigated salivary concentrations of other

members of the interleukin-1 family of cytokines in periodontitis. Interleukin-1 has been

detected in saliva but the levels are not altered in periodontitis [31]. Although interleukin-1

receptor anatagonist has well-defined antagonist roles in immunoregulation [36, 109], there

are currently no reports of the investigation of salivary interleukin-1 receptor anatagonist in

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periodontitis. Interleukin-18 is secreted by a number of cell types (myeloid cells,

keratinocytes, epithelial cells) and by activating neutrophils and Th1 cells potentially plays

an important role in periodontal inflammation (reviewed in [7]). Increased concentrations of

interleukin-18 were found in the saliva of periodontitis patients compared to healthy

controls [74]. Salivary concentrations of interleukin-33, an interleukin-1 family member that

drives Th2 cytokine responses, appear to remain unaffected by periodontitis [18] (Table 1).

Although the role of other, newly identified members of the interleukin-1 family in chronic

inflammatory diseases is receiving increasing attention [79] nothing is known about salivary

concentrations of these novel cytokines.

Tumor necrosis factor-α

The tumor necrosis factor-α superfamily comprises some 19 ligands identified on the basis

of gene sequence homology [1]. A survey of the various physiological roles of these ligands

reveals wide-ranging roles in cell proliferation, apoptosis and morphogenesis as well as host

immune defence [1]. However, with the exception of tumour necrosis factor-α and receptor

activator of nuclear factor kappa-B ligand (see below) there is little or no information about

the potential of the tumour necrosis factor superfamily members as biomarkers of

periodontitis. Tumor necrosis factor-α is a key pro-inflammatory cytokine and central to

anti-microbial immunity at a number of levels. The fundamental physiological role of this

cytokine beyond the immune system is now established and tumor necrosis factor-α has

numerous homeostatic functions in human physiology including a role in neurological

regulation [91]. Tumor necrosis factor-α is produced mainly by macrophages as a primary

response of Toll-like receptor signalling, by activated T cells and NK cells and also by non-

immune cells including endothelial cells and fibroblasts [91]. The long-standing success of

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anti- tumor necrosis factor-α therapy in rheumatoid arthritis has heightened interest in this

important cytokine in inflammatory diseases such as periodontitis.

Tumor necrosis factor-α and receptor activator of nuclear factor kappa-B ligand both

belong to the tumour necrosis factor superfamily, and both are potent activators of

osteoclasts and thereby mediate bone resorption [39]. Some 7 studies have investigated

salivary tumour necrosis factor-α in periodontal health and disease (Table 2) however,

tumor necrosis factor-α is either undetectable or the levels detected in saliva are very low.

Frodge et al [33] reported significantly higher salivary tumour necrosis factor-α

concentrations in periodontitis compared to periodontal health using a highly sensitive

detection methodology, but the levels detected in the different patient groups were

nonetheless very low (<4.3pg/ml). Although after periodontal treatment salivary tumor

necrosis factor- α concentrations were reported to decrease [92] there is no evidence that

salivary tumour necrosis factor- α levels correlate with clinical changes in the periodontium

either during disease progression [89] or after treatment [92]. Currently, therefore, it seems

unlikely that salivary tumor necrosis factor-α can be regarded as a good candidate

biomarker for periodontal disease.

Interleukin-6

Interleukin-6 is produced by innate immune cells such as macrophages and dendritic cells

but also by some CD4+ T-cells as well as non-immune cells such as fibroblasts and

endothelial cells [84]. Interleukin-6 is elevated in many inflammatory diseases and mainly

functions to activate B-lymphocytes in addition to having a role in influencing the balance of

CD4+ effector T-cell populations and potentially influencing myeloid cell differentiation [84].

Interest in interleukin-6 has recently increased as anti-interleukin-6 blockade has proven to

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be efficacious in the treatment of rheumatoid arthritis [48, 84]. Levels of interleukin-6

measured in saliva are low in both periodontal health and disease [29]. Also, while some

studies have shown that salivary interleukin-6 concentrations are significantly higher in

periodontitis than in healthy individuals, others found no differences between the two

groups (Table 2). Reports on post-treatment evaluations of salivary interleukin-6

concentrations showed no significant changes [54, 77]. Therefore, interleukin-6 is not a

robust salivary periodontitis biomarker. Interleukin-6 is a member of a family of cytokines

which have overlapping biological functions and binding receptors which share a common

subunit (gp130); these include leukaemia inhibitory factor, interleukin-11 and oncostatin M

[84] although these mediators are yet to be assessed as potential salivary biomarkers for

periodontal disease.

Interleukin-4

Interleukin-4 is mainly produced by T-cells, specifically the Th2 subset of T-cells and has

important functions in the regulation of cell proliferation and apoptosis in numerous cell

types, including lymphocytes, myeloid cells, fibroblasts and epithelial cells [59]. Interleukin-4

is a critical mediator in regulating T-cell development and plasticity in periodontal disease

[32]. However, interleukin-4 is only present at very low concentrations in saliva and findings

relating to salivary concentrations of interleukin-4 in periodontitis are inconsistent: one

study reported significantly higher interleukin-4 concentrations in periodontitis [77] and two

studies find no differences in salivary interleukin-4 concentrations between periodontal

health and disease [80, 103]. Similarly, although one study reported a treatment-related

reduction in salivary interleukin-4 levels [77], other studies do not report any evidence for

post-treatment changes in salivary interleukin-4 in periodontitis patients [31, 89]. Therefore

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interleukin-4 cannot, as yet, be considered a viable salivary biomarker for periodontal

disease.

Interleukin-10

The interleukin-10 family of cytokines has some nine members of which interleukin-10 itself

is the most studied [73, 88]. Interleukin-10 is widely produced by innate and adaptive

immune cells alike and is a key anti-inflammatory cytokine, involved in inhibiting and

regulating pro-inflammatory immune responses and promoting resolution of inflammation

[73]. However, data from clinical studies have been inconsistent (Table 2): one study

reported significantly lower levels in periodontitis compared to controls [77] and others

have found no differences in salivary interleukin-10 concentrations between periodontal

health and disease [80, 103] (Table 2). Periodontal treatment does not appear to affect

salivary interleukin-10 concentrations [54, 77] and initial levels do not correlate with disease

progression [89]. Thus, evidence from clinical studies does not support the notion that

interleukin-10 is a useful salivary biomarker in periodontitis.

Interleukin-17

Since the discovery of the interleukin-17 secreting T-cell subset Th17, the dogma of the

central importance of Th1 cytokines driving the “early”, stable and reversible gingivitis

lesions versus Th2 cytokines driving the “later”, progressive periodontitis lesions has been

challenged [34]. Interleukin-17 is produced by Th17 cells after activation of the adaptive

immunity and serves to reinforce pro-inflammatory immune responses [35]. Although

salivary interleukin-17 concentrations are minute in both periodontal health and disease

[74], two studies comparing interleukin-17 in periodontal health and disease reported

significantly lower concentrations in the latter [74, 77] (Table 2). However, salivary

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interleukin-17 concentrations did not change after periodontal treatment [77]. In contrast,

in a comparative analysis of different IL-17 isoforms, interleukin-17A, interleukin 17E and

the interleukin-17A/F ratio was found to be significantly elevated in periodontitis patients

[6]. It is currently unclear, therefore, whether interleukin-17 is a suitable periodontitis

biomarker, although this seems unlikely given the very low concentrations present in saliva.

Other immunoregulatory cytokines

No differences between salivary concentrations of granulocyte-macrophage colony-

stimulating factor, interleukin-2, interleukin-5, interleukin-13 or interferon-γ between

periodontal health and disease, or changes after periodontal treatment, have been reported

(Table 2).

Chemokines

Chemokines target a wide range of immune cells, including neutrophils, lymphocytes and

macrophages to facilitate their recruitment to the site of inflammation, and they are a key

player in the pathogenesis of periodontitis [94, 107]. At least human 44 chemokines have

thus far been identified and these are divided into 4 families on the basis of structural

homology [107]. Chemokines such as macrophage inflammatory protein-1 (CCL3),

interleukin-8 (CXCL8) and CXCL10 are produced by oral cells and tissues in response to pro-

inflammatory cytokines such as interleukin-1, tumour necrosis factor-α or interferon-γ, and

bacterial products such as lipopolysaccharide or fimbriae [8, 79, 94]. Salivary concentrations

of the neutrophil chemotactic factor interleukin-8 do not appear to be altered in

periodontitis or following periodontitis treatment [81, 92, 103] (Table 2). Significantly higher

salivary concentrations of the Th1-cell chemotactic factor macrophage inflammatory protein

1 and the monocyte chemotactic protein-1 (also called CCL2) and have been found in

15

periodontitis compared to periodontal health [2, 40]. A decrease in salivary macrophage

chemotactic protein-1 concentrations was found after periodontal treatment [40]; however,

no change was observed for macrophage inflammatory protein-1 [92]. Currently,

therefore, only macrophage chemotactic protein-1 seems to hold promise as a potential

biomarker for periodontitis, but for the vast majority of chemokines there is no information

available on salivary concentrations in periodontitis.

Cytokines involved in bone cell regulation

The receptor activator of nuclear factor kappa-B ligand-osteoprotegerin system has been

the focus of many recent investigations in periodontal research. Receptor activator of

nuclear factor kappa-B ligand is mainly produced by osteoblasts, fibroblasts and activated T

and B cells. When binding to its receptor on pre-osteoclasts, receptor activator of nuclear

factor kappa-B ligand induces osteoclast differentiation and bone resorption [39]. The decoy

receptor osteoprotegerin is largely produced by fibroblasts and, through binding of receptor

activator of nuclear factor kappa-B ligand, inhibits osteoclastogenesis [9]. In addition to

roles in bone metabolism, these cytokines influence immune responses, emphasising the

emerging paradigm that these two systems share common regulatory factors [9, 39].

Generally, it appears that receptor activator of nuclear factor kappa-B ligand is upregulated

and osteoprotegerin is downregulated in periodontal disease [11, 17]. However, the results

of the analysis of salivary concentrations of osteoprotegerin and receptor activator of

nuclear factor kappa-B ligand in periodontal health and disease are not consistent. Out of

eight studies, only three [16, 99, 105] simultaneously measured salivary concentrations of

osteoprotegerin and receptor activator of nuclear factor kappa-B ligand (Table 3). Two of

these studies showed a significant increase in receptor activator of nuclear factor kappa-B

16

ligand concentrations in periodontitis compared to periodontal health, but, of these two

studies only Tobon-Arroyave et al. [105] noted lower levels of osteoprotegerin in

periodontitis. Only one other study [80] recorded lower osteoprotegerin concentrations in

periodontal disease as compared to health (Table 3). Two studies have investigated post-

treatment salivary concentrations of osteoprotegerin and both found a significant reduction

after periodontal treatment [54, 92]. Salivary concentrations of receptor activator of nuclear

factor kappa-B ligand have not been investigated after periodontitis treatment to date.

Osteoprotegerin and receptor activator of nuclear factor kappa-B ligand show great promise

as biomarkers of rheumatoid arthritis [110]. However, salivary concentrations of both

osteoprotegerin and receptor activator of nuclear factor kappa-B ligand are relatively small

and results from studies of periodontal disease are inconsistent. More data are needed to

judge if salivary levels of receptor activator of nuclear factor kappa-B ligand and

osteoprotegerin are suitable periodontitis biomarkers. Calprotectin [80] may be elevated in

periodontitis but these studies are yet to be independently replicated.

Growth factors

So-called growth factors have been regarded as multifunctional, being involved in several

physiological processes such as tissue development, regeneration and wound healing. More

recently, these molecules have been revealed to have more specific roles in immune

responses and inflammatory diseases. The paradigm for this has been transforming growth

factor- that has numerous clearly defined roles in immunoregulation including regulation

of T-cell subsets [32]. Although elevated levels of transforming growth factor- and other

growth factors have been reported in gingival crevicular fluid [37] there are only very limited

similar studies of saliva (and no available data on salivary transforming growth factor- in

17

periodontitis). One exception is hepatocyte growth factor which has been shown to

enhance the production of matrix metalloproteinases and stimulates processes of wound

healing such as vascularisation and keratinocyte proliferation [28, 61] and, more recently,

numerous roles in inflammation and immunity have been revealed [65]. In oral tissues,

hepatocyte growth factor is produced by periodontal ligament cells and gingival fibroblasts

[71] in response to pro-inflammatory cytokines such as interleukin-1, tumor necrosis factor-

α as well as prostaglandin E2 and bacterial lipoteichoic acid [70, 98]. It is suggested the

hepatocyte growth factor may drive apical migration of epithelial cells in periodontitis [49,

70, 71]. Cross-sectional studies investigating salivary concentrations of hepatocyte growth

factor consistently report elevated concentrations in periodontal disease compared to

periodontal health and 2 longitudinal studies provide further substantial evidence in

support of the potential of hepatocyte growth factor as a biomarker for periodontitis (Table

4).

Adipokines

Adipokines comprise over 600 biologically active molecules produced by adipose tissue

which include a wide range of pro- and anti-inflammatory cytokines (interleukin-1, tumour

necrosis factor-, interleukin-6, interleukin-10) , growth factors (such as vascular

endothelial growth factor and transforming growth factor- hormones (leptin,

adiponectin, visfatin, resistin) and chemokines (monocyte chemotactic protein-1,

interleukin-8) [58]. These molecules have wide-ranging and overlapping roles in physiology

and there is an increasing recognition of their importance in immune-regulation and

inflammatory disease [25, 69]. Many of the aforementioned adipokines are also synthesised

by other cell types and are considered in other sections of this review.

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The archetypal adipocyte-derived adipokine is leptin, which has well-defined pro-

inflammatory functions and is one potential mediator linking metabolic disease with

common co-morbidities such as periodontitis and rheumatoid arthritis [22, 69, 101].

However, although there is some information implicating leptin in the pathogenesis of

periodontitis there have been no studies of salivary leptin levels. However, the adipokines

visfatin and chemerin have been found in significantly higher salivary concentrations in

periodontitis compared to periodontal health [75, 100] (Table 5). Interestingly, although

there is little direct evidence for a role of these adipokines in periodontitis pathogenesis,

they do have relevant biological immunoregulatory functions; for example, visfatin does

induces tumour necrosis factor-α, interleukin-6 and interleukin-1β in monocytes and

fibroblasts and also acts as a chemoattractant for monocytes and B-cells [14, 66] and

chemerin acts as a chemoattractant for various immune cells such as macrophages,

dendritic cells and NK cells during inflammation [118].

Identification of cytokine biomarkers from proteomic analysis of saliva

The majority of studies investigating possible biomarkers for periodontal disease have used

a ‘candidate’ biomarker approach exploiting existing information about disease

pathogenesis. However, the proteome of human saliva has been characterised and is

providing information underpinning a global, unbiased analytical approach to biomarker

identification, an endeavour enhanced by recent developments in protein separation and

mass spectrometry [24, 90]. This approach has been exploited in the identification of

biomarkers for oral diseases including Sjogren’s syndrome and oral cancer [45, 46].

Although such proteomic studies have identified elevated levels of two of the S100 family of

calcium binding proteins (which have cytokine-like activity in the regulation of

19

inflammation) [55, 86, 116] problems of sensitivity, recovery and processivity likely explain

the limited data from proteomic studies relating to low abundance proteins such as

cytokines [102].

Cytokines as an element of biomarker signature analysis

It is generally accepted that the immunopathogenesis of periodontal disease is driven by

complex and dynamic networks of cytokine interactions, which exhibit considerable inter-

individual variation rather than being dominated by the action of individual cytokines [52,

79]. However cross-sectional analysis of multiple cytokines has generally not revealed

consistent evidence for a ‘biomarker signature’ (e.g. Table 2). Cytokines are present in

significantly lower concentrations in saliva than other mediators, for example enzymes such

as matrix metalloproteinases, and therefore subtle changes may not always be detected.

Indeed there is some evidence that analytical techniques with greater sensitivity and linear

range may unmask periodontitis-associated biomarkers not detected by conventional

enzyme-linked immunosorbant assays, which are almost universally used in research in this

field [21]. Nonetheless, there have been some encouraging recent studies, which suggest

that simultaneous analysis of a combination of interleukin-1, interleukin-6 and matrix

metalloproteinase-8 strongly distinguished periodontitis patients from healthy individuals

[29]. Also, differences in periodontal disease severity were efficiently detected by a

combination of matrix metalloproteinase-8, matrix metalloproteinase-9, osteoprotegerin

and calprotectin assays coupled with quantification of the periodontal pathogens

Porphyromonas gingivalis and Treponema denticola in dental plaque [80]. Further studies

revealed the combined levels of plaque bacterial biomarkers and salivary mediators (matrix

metalloproteinase-8, matrix metalloproteinase-9, osteoprotegerin and interleukin-1)

20

successfully predicted progression of periodontal disease for the majority of patients

studied [54].

The intriguing concept that the biomarker tests required to screen for disease

susceptibility and those which will be useful for monitoring response to therapy will be

different has been raised, although this remains to be systematically investigated [108].

There is a wide variety of multiplex analysis platforms available but, unlike the popular

sandwich enzyme-linked immunosorbant assays, no one multiplex approach has been

universally adopted. Indeed, concerns about imprecision and analyte recovery for some of

these platforms have been highlighted and careful comparison with ‘gold standard’ ELISA

methods is recommended before multiplex approaches are used in quantitative analysis of

salivary mediators [15]. It is likely that many of these techniques are semi-quantitative at

best and might only be considered of use for biomarker discovery.

The effect of systemic diseases on salivary cytokine concentrations

Few studies have investigated the impact of systemic diseases on salivary cytokines in

periodontitis and none have been designed specifically with this aim in mind. Therefore, one

has to be cautious when drawing conclusions from the presented study results. Reports on

salivary interleukin-1β concentrations in heart disease, hypertension, diabetes,

inflammatory bowel disease or rheumatoid arthritis show no significant differences

between patients and systemically healthy controls [64, 81, 117] even though oral health

status was not always considered in the analysis [81]. A comparable theme can be seen for

salivary interleukin-6 and tumour necrosis factor-α; concentrations of both do not appear to

be affected by systemic conditions [3, 23, 64, 81, 96]. An investigation of the salivary

concentrations of interleukin-1 receptor antagonist in diabetes revealed lower

21

concentrations in patients with periodontal disease as compared to those with periodontal

health [19]. Since a non-diabetic control group was not included in this study, it is not

possible to estimate whether local or systemic conditions influenced the levels of salivary

interleukin-1 receptor antagonist. Inflammatory bowel disease was associated with higher

salivary interleukin-8 concentrations in one study, however since oral health status was not

considered, it is not possible to evaluate local or systemic effects independently [81].

Similarly, an increase in salivary monocyte chemoattractant protein-1 concentrations in

Sjögren's syndrome cannot be accounted for without evaluation of oral health [43]. The

same study also investigated salivary macrophage inhibitory protein-1α concentrations and

found no differences between patients with Sjögren's syndrome and systemically healthy

controls [43].

The effect of smoking on salivary cytokine concentrations

Smoking is an established risk factor for periodontal disease [106] and it is reasonable to

hypothesise that smoking may influence salivary cytokine levels. Salivary concentrations of

cytokines such as interleukin-1β and interleukin-6 do not appear to be affected by smoking

although the concentrations of other salivary proteins were lower in smokers [51, 68, 81,

82]. However, decreased concentrations of interleukin-8 in smokers compared to non-

smokers have been noted [81, 82]. One study found no association between receptor

activator of nuclear factor kappa-B ligand or osteoprotegerin concentrations and smoking

[105]. However, in contrast, higher receptor activator of nuclear factor kappa-B ligand and

lower osteoprotegerin concentrations in the saliva of smokers with periodontitis compared

to non-smokers with periodontitis have been reported [16]. Significantly elevated levels of

hepatocyte growth factor were found in smokers with periodontitis as compared to non-

22

smokers with periodontitis [4]. Considering the available data, it appears that smoking only

has a limited impact on salivary cytokine concentrations. However, more research, which

specifically aims to evaluate these relationships, is needed to come to firm conclusions.

Concluding remarks

The present review demonstrates that saliva is a valid local source for investigating

periodontal cytokines, with many advantages over other sources such as gingival crevicular

fluid. Care has to be taken when considering which methodology to choose for analysis;

thus, not all commercial manufacturers of enzyme-linked immunosorbant assay kits provide

quality control data for use with saliva samples and the utility of multiplex analysis remains

somewhat uncertain. Also, despite the fact that it is established that cytokines are elements

of complex and dynamic networks [93], precise interpretation of quantitative data above

and beyond correlation analyses remains challenging.

The majority of studies of salivary cytokines in periodontitis have either not yielded

evidence for disease associations, or have yielded inconsistent results or positive

correlations, which remain to be substantially replicated in independent studies.

Nevertheless, current evidence suggests that of the molecules with cytokine-like activity

studied thus far, interleukin-1β and hepatocyte growth factor are the most robust salivary

biomarkers for periodontal disease. High quality research designs specifically targeting

sensitivity and specificity and confounding factors such as smoking and systemic diseases

should be the next step in salivary periodontitis biomarker research. This would help to

evaluate if the biomarker knowledge can be implemented in a diagnostic test to detect

periodontitis (or risk thereof) before periodontal tissue breakdown occurs. Although there

is much interest in developing ‘chairside’ tests for salivary mediators using novel

23

technologies, it is critical that such research is supported by substantial laboratory and

clinical data confirming the clinical utility of individual candidate biomarkers.

Acknowledgements

The authors declare that they have previous and current funding from Philips Oral

Healthcare in relation to studies of salivary biomarkers in periodontitis.

24

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118. Zabel BA, Kwitniewski M, Banas M, Zabieglo K, Murzyn K, and Cichy J. Chemerin regulation and role in host defense. Am J Clin Exp Immunol 2014: 3: 1-19.

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Table 1. Studies of IL-1 family cytokines as candidate salivary biomarkers for periodontitis.

Reference Study Type Patient groups IL-1 cytokine Principal findings

[63] Case-control, cross-sectional

28 periodontitis patients; 29 healthy controls

IL-1 Significantly higher levels in periodontitis group (mean ± SD; 753.7 ± 1022.4 pg/ml) as compared to controls (212.8 ± 167.4 pg/ml); significant positive correlation with bleeding on probing and clinical attachment loss measurements.



IL-1 No significant difference between groups.

[67] Cross-sectional with no controls

98 periodontitis patients IL-1 Positive association between levels of IL-1β and alveolar bone loss.

[89] Case-control, longitudinal study

40 periodontitis patients with bone loss after 5 years; 40 controls with no bone loss

IL-1 Positive association with IL-1β and extent of alveolar bone loss over a 5-year period.



IL-1 Significantly higher levels in periodontitis group as compared to controls.



IL-1 No significant difference between groups


8 localised aggressive periodontitis patients with Aggregatibacter actinomycetemcomitans,

IL-1, IL-1 IL-1β elevated in children who developed bone loss over a 2-3 year period compared to those who remained healthy.

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20 healthy controls with A. actinomycetemcomitans and 20 healthy controls without A. actinomycetemcomitans



IL-1 Significantly higher levels in periodontitis group (Mean ± SD; 665.7 ± 267.5 pg/ml) as compared to controls (467.8 ± 279.8 pg/ml).


49 periodontitis patients; 32 gingivitis patients; 18 healthy controls

IL-1 No significant difference between groups.



IL-1 Significantly higher levels in periodontitis group as compared to controls (mean; 33.11 pg/ml). Precise values for IL-the periodontitis group not presented.


21 healthy adults; 52 chronic periodontitis patients

IL-18 Significantly increased concentration of IL-18 in periodontitis (mean ± SD; 275.05 ± 289.46 pg/ml) as compared to controls (143.71 ± 103.68 pg/ml). Significant positive correlation of IL-18 levels with bleeding on probing measurements.


28 periodontitis patients; 24 healthy controls. Patients monitored 1 month after treatment (oral health instruction, scaling and

IL-1 Significantly higher levels in periodontitis group (mean ± SD; 1312.75 ± 691.22 pg/ml) as compared to controls (161.51 ± 149.6 pg/ml). Significant positive correlation of IL-1on probing, gingival index and plaque

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root planning). index measurements. Levels of IL-

in volunteers with moderate to severe periodontitis.


17 moderate to severe periodontitis patients; 24 mild periodontitis patients, 23 gingivitis patients; 15 healthy controls

IL-1 Disease monitoring over 6 months (no treatment) then 6 months during which time treatment (oral health instruction, scaling and root planing as appropriate) was provided. No changes during monitoring phase but levels of IL-

in volunteers with moderate to severe periodontitis.


192 subjects with and without tye-2 diabetes mellitus

IL-1 Levels correlate with clinical parameters of periodontal disease.

[92] Longitudinal 68 periodontitis patients. 35 patients undergoing both scaling and root-planing and oral hygiene instruction as compared to 33 patients having oral hygiene instruction alone

IL-1 IL-received both scaling and root planing and oral hygiene instruction.



IL-33 No significant difference in IL-33 levels between groups.

[82] Case-control, 49 severe periodontitis IL-1 Significantly higher levels in severe

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cross-sectional patients; 89 mild periodontitis patients; 303 healthy controls

periodontitis group (mean ± SD; 144 ± 220 pg/ml) and mild periodontitis (82 ± 109 pg/ml) as compared to controls (61 ± 87pg/ml). Significant positive correlation with bleeding on probing. No differences between smokers or ex-smokers.


30 healthy adults; 50 severe periodontitis patients

IL-1 Significantly higher levels in periodontitis group (mean ± SD; 90.94 ± 85.22pg/ml) as compared to controls (7.24 ± 7.69 pg/ml).


340 no to mild periodontitis; 123 moderate to severe periodontitis; 30 edentulous

IL-1 Levels significantly associated with all periodontitis parameters, presence of coronary artery disease and diabetes (but not smoking or ex smokers). Significantly lower levels in edentulous patients.

IL-1, interleukin-1; IL-1interleukin-1; IL-18, interleukin-18; IL-33, interleukin-33.

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Table 2. Studies of immunoregulatory cytokines as candidate salivary biomarkers for periodontitis.

Reference Study type Patient groups Cytokines Principal findings


30 moderate periodontitis patients and 9 severe periodontitis patients. Both groups were HIV+

IL-2, IL-4, IL-10, IL-12,

IFN-

No significant differences between groups for any cytokine.

[5] Cross-sectional, case-control


TNF- TNF-not detected.

[67] Longitudinal 98 periodontitis patients IL-6 No association with alveolar bone loss.

[89] Longitudinal, case-control


IFN-TNF-

IL-4, IL-6, IL-8

No association with alveolar bone loss.



TNF- Significantly higher levels in periodontitis group (mean ± SEM; 4.33 ± 0.73 pg/ml) as compared to controls (2.03 ± 0.49 pg/ml).

[31] Longitudinal, case-control

8 localised aggressive periodontitis patients with A. actinomycetemcomitans, 20 healthy controls with A. actinomycetemcomitans and 20 healthy controls without A.

21 cytokines including IL-2, IL-3, IL-4, IL-5, IL-6, IL-8, IL-9, IL-10, IL-12, IL-13, IL-17, RANTES, MCP-1, IFN-

MIP-1 elevated in children who developed bone loss compared to those who remained healthy.

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actinomycetemcomitans Screening at 6 month intervals for 2-3 years

TNF-

GM-CSF



IL-6; TNF- No significant differences between groups for either cytokine.



GM-CSF; IL-2; IL-4; IL-5; IL-6; IL-8; IL-10; IFN-

TNF-

No significant differences between groups for any cytokine. Significant negative correlation between IL-10 levels and bleeding on probing measurements. Significantly positive correlation between IL-8 and bleeding on probing measurements.


49 periodontitis patients; 32 gingivitis patients; 18 healthy controls

IL-2, IL-4, IL-5, IL-6, IL-10, IL-13, TNF-

IFN-

IL-5 and IFN-differences between groups for the other cytokines.



IL-6 Significantly higher levels in periodontitis group as compared to controls. Values for IL-6 concentrations not presented.



IL-17 Significantly lower levels of IL-17 in periodontitis group as compared to controls.



TNF- No significant differences between groups.

[92] Longitudinal 68 periodontitis patients. 35 patients undergoing both scaling

TNF- IL-8,

MIP-1

TNF-groups, MIP-compared to non-responders.

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and root-planing and oral hygiene instruction as compared to 33 patients having oral hygiene instruction alone.



MIP-1

Significantly higher levels of MIP-1 in periodontitis group as compared to

controls. No values for MIP-1 levels presented. Significantly positive

correlation between MIP-1 levels and both bleeding on probing and clinical attachment loss measurements.

[77] Longitudinal study, cross-sectional

20 periodontitis patients; 20 healthy controls. Screening 6 weeks after scaling and root-planing.

IL-4, IL-6, IL-10, IL-17

Significantly higher levels of IL-4, IL-6, and significantly lower levels of IL-10 and IL-17 in periodontitis group as compared to controls. IL-4 levels reduced to those of healthy volunteers 6 weeks after treatment. Cytokine concentrations not presented.


49 severe periodontitis patients; 89 periodontitis patients; 303 healthy controls

IL-6, IL-8 No significant differences between groups. Significantly positive correlation between IL-8 and bleeding on probing measurements.


30 healthy adults; 50 severe periodontitis patients

TNF-IL-6,

IFN-

Significantly higher levels of IL-6 in periodontitis group (mean ± SD; 35.57 ± 48.17 pg/ml) as compared to controls (3.30 ± 2.32 pg/ml); significantly lower

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levels of IFN-in periodontitis group (3.19 ± 2.29 pg/ml) as compared to controls (27.65 ± 48.29 pg/ml).

[40] Cross-sectional, longitudinal

30 periodontitis patients; 15 healthy controls. Screening 6 weeks after scaling and root-planing.

MCP-1 Significantly higher levels of MCP-1 in periodontitis group as compared to controls. Values for MCP-1 concentrations not presented.

[6] Cross-sectional, Case-control


IL-17A, IL-17E, IL-17F

IL-17A, IL-17 E and IL-17A/F ratio significantly elevated in periodontitis patients and positively correlated with various clinical parameters of periodontitis.

GM-CSF, granulocyte macrophage-colony stimulating factor; IFN-, interferon-; IFN-, interferon-; IL-2, interleukin-2; IL-2, interleukin-3; IL-3,

interleukin-4; IL-4, interleukin-4; IL-5, interleukin-5; IL-6, interleukin-6; IL-8, interleukin-8; IL-9, interleukin-9I; IL-10, interleukin-10; ; IL-12,

interleukin-12; ; IL-13, interleukin-13; ; IL-17, interleukin-17; RANTES, regulated on activation, normal T expressed and secreted; MCP-1,

monocyte chemoattractant protein-1; MIP-1, macrophage inflammatory protein1

and secreted; TNF-, tumor necrosis factor-.

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Table 3. Studies of bone regulating cytokines as candidate salivary biomarkers for periodontitis.

Reference Study type Patient groups Cytokines analysed

Principal findings

[63] Cross sectional, case control


OPG No significant difference between groups.

[16] Cross sectional 67 untreated chronic periodontitis patients; 44 chronic periodontitis patients on maintenance therapy

OPG, RANKL OPG levels correlated with clinical measures of periodontitis.



RANKL Only detected in a minority of subjects and no significant difference between groups.


21 moderate-severe periodontitis patients; 28 mild periodontitis patients; 32 gingivitis patients; 18 healthy controls

OPG, calprotectin

Significantly lower levels of both cytokines in periodontitis (mild and severe) as compared to controls (gingivitis and healthy). OPG levels were (median, range): moderate-severe periodontitis 1.9 pg/ml (0.2-10); mild periodontitis: 1.6 pg/ml; (0.5-11.8); gingivitis: 2.7 pg/ml (1.2-6.2); healthy: 2.3 pg/ml (1.4-6.6). Calprotectin levels were moderate-severe periodontitis 5.4 pg/ml (1.7-97.6); mild periodontitis: 4.3 pg/ml (0-

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17.8); gingivitis: 3.5 pg/ml (0-24.6); healthy: 3.0 pg/ml (1.3-10).



OPG No significant difference between groups

[54] Longitudinal, case control

17 moderate to severe periodontitis patients; 24 mild periodontitis patients, 23 gingivitis patients; 15 healthy controls

OPG, calprotectin,

No changes during monitoring phase but levels of OPG declined significantly after 6 months treatment in volunteers with moderate to severe periodontitis. Data presented in graphical form only.

[92] 68 periodontitis patients: 35 patients undergoing both scaling and root planning and oral hygiene instruction as compared to 33 patients having oral hygiene instruction alone.

OPG OPG levels declined in both treatment groups. Data presented in graphical form only.



OPG, RANKL Significantly higher levels of RANKL and significantly lower levels of OPG in periodontitis group as compared to controls. Levels are (median, inter-quartile range): OPG: periodontitis 95.2 pg/ml (49.8-145.2); healthy 131.6 pg/ml (82.2-202.4); RANKL: periodontitis 6.0 pg/ml (2.7-11.1);

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healthy 4.0 pg/ml (2.4-6.6).


25 chronic periodontitis patients; 25 healthy controls

OPG, RANKL Significantly higher levels of RANKL in the periodontitis group (266 ± 48 pg/ml; median ± interquartile range) as compared to controls (207 ± 83 pg/ml). No significant difference in OPG levels between groups.

OPG, osteoprotegerin; RANKL, receptor activator of nuclear factor-kappaB ligand.

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Table 4. Studies of growth factors as candidate salivary biomarkers for periodontitis.

Reference Study type Patient groups Growth factor Salivary growth factor levels


32 periodontitis patients and 12 healthy controls

VEGF Significantly higher levels in periodontitis group as compared to controls. Data presented in graphical form only.

[89] Longitudinal, case control


HGF Positive association with HGF and extent of alveolar bone loss over a 5 year period.



HGF Significantly higher levels in periodontitis group (Mean, range: 1.87 ng/ml; 0.06-5.38) as compared to controls (0.68 ± ng/ml; 0-7.33). HGF levels correlated with gingival index, plaque index and papillary bleeding index.


12 severe periodontitis patients; 12 moderate periodontitis; 12 healthy controls

HGF Significantly higher levels in severe periodontitis group (mean ± SD; 3430.8 ± 1640.2) and moderate periodontitis group (1878.99 ± 1713.54 pg/ml) as compared to controls (443.82 ± 295.14 pg/ml).



HGF Significantly higher levels in periodontitis group as compared to controls. Data presented previously (see above)

HGF, hepatocyte growth factor; VEGF, vascular endothelial growth factor.

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Table 5. Studies of adipokines as candidate salivary biomarkers for periodontitis.

Reference Study type Patient groups Adipokine Principal findings

[75] Cross sectional, case-control

25 patients with chronic periodontitis, 24 patients with gingivitis, 25 healthy controls

Chemerin, visfatin, progranulin

and TNF-

Chemerin was significantly elevated in the periodontitis group (median, range: 0.084ng/ml, 0.063-0.105) but not the gingivitis group (0.042ng/ml, 0.042-0.063) as compared to controls (0.042ng/ml, 0.023-0.063). Visfatin was significantly elevated in both the periodontitis group (589 ng/ml, 302-1195) and the gingivitis group (791 ng/ml, 267-1127) as compared to controls (267 ng/ml (125-616). Levels of chemerin but not visfatin correlated with clinical attachment loss measurements.

[100] Cross sectional, case-control

20 patients with chronic periodontitis, 20 healthy controls

Visfatin Visfatin significantly elevated in periodontitis (mean ± SD: 33.43 ± 15.72 ng/ml) as compared to controls (23.38 ± 7.58 ng/ml).

TNF-, tumor necrosis factor-.

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