Biomarkers in Gingival Diseases: Current Insights and Future Perspectives

Abstract

Periodontal diseases represent a spectrum of gingival disorders with multifaceted etiologies. Identifying and utilizing biomarkers in these conditions are essential for early detection, risk stratification, and personalized therapeutic interventions. This chapter provides a comprehensive overview of biomarker research in gingival diseases, emphasizing clinical applications, detection methods, and the potential of saliva and gingival crevicular fluid as diagnostic vehicles. We also delve into emerging research areas such as microbiome-associated, epigenetic, and metagenomic biomarkers. The chapter underscores the challenges associated with biomarker validation, the promise of multi-marker panels for improved accuracy, and the potential of longitudinal studies to predict disease progression. As point-of-care technologies and wearables pave the way for future diagnostics, innovative solutions like biosensors and micro-electro-mechanical systems (MEMS) are highlighted. This chapter encapsulates the importance of advancing biomarker discovery and its pivotal role in reshaping gingival disease management.

1. Introduction

In a state of health, gingival tissues withstand a persistent microbial onslaught proficiently countered by an efficient immune surveillance mechanism. Any deviation from this equilibrium engenders gingival diseases. Alarmingly, the worldwide prevalence of gingivitis exceeds 80% [1]. Transformations in gingival hue, shape, consistency, location, and superficial texture clinically characterize gingivitis. The most definitive clinical indicator of gingivitis is bleeding upon probing.

Gingival diseases can be dichotomized into dental plaque biofilm-induced gingivitis and non-dental plaque-induced gingival diseases. Dental plaque-induced gingivitis can be characterized at the site-specific level as “an inflammatory lesion, engendered by the dynamic interplay between dental plaque biofilm and the host’s immune-inflammatory reaction [2]. Importantly, this inflammation remains confined within the gingival domain without transgressing into the periodontal attachment. This localized inflammatory response remains restrained, never exceeding the mucogingival boundary, and is fully reversible upon mitigating levels of dental plaque proximal and below the gingival border. Although dental plaque microbes are the primary etiological agents, the pathogenesis of the disease is intrinsically modulated by myriad factors. Local determinants such as plaque-retentive anomalies and oral xerostomia, along with systemic modulators like tobacco use, hyperglycemia, malnutrition, pharmaceuticals, endocrinological fluctuations, especially during puberty, pregnancy, or due to oral contraceptive intake, and hematological anomalies act as key contributors. Moreover, environmental, systemic, genetic, and epigenetic factors further modulate the disease mechanism. From a clinical perspective, a case is earmarked as gingivitis when over 10% of sites demonstrate bleeding and possess a probing pocket depth of less than 3 mm [3].

Contrastingly, gingival diseases that are non-plaque induced do not ameliorate upon plaque eradication. These maladies can either be exclusively oral manifestations or symptomatic of systemic ailments. Gingival diseases that are not plaque-induced present a unique challenge as they do not improve upon plaque removal. These conditions may manifest in the oral cavity or indicate underlying systemic issues. They encompass a spectrum: genetic variations, infections caused by specific pathogens such as bacteria, viruses, or fungi, to immune-inflammatory issues, including hypersensitivity reactions, skin and mucosal autoimmune conditions, and granulomatous and reactive lesions [4]. Additionally, there are neoplastic lesions, disorders arising from hormonal, metabolic, or nutritional imbalances, trauma-related lesions, and even unusual gingival pigmentation. This chapter is dedicated to a deeper exploration of biomarkers within these gingival disease contexts, providing a contemporary understanding and suggesting future research directions.

2. Biomarkers in diseases

With their inherent molecular specificity, Biomarkers occupy a cardinal position in periodontics, serving as pivotal tools in disease diagnosis, prognosis, and therapeutic monitoring. Within the ambit of periodontics, biomarkers can be delineated based on their inherent stability: while biochemical and microbiological markers are static, encapsulating a point-in-time depiction of disease processes, histopathological and genetic markers are dynamic, reflecting continuous and evolving changes in tissue or genetic configurations [5].

Biomarkers are further segregated based on their functional relevance from physiological health to pathological disease state. Such stratifications are elucidated as follows:

Susceptibility Biomarkers: These markers occupy the earliest echelon of biomarker relevance, identifying individuals who may be predisposed to the onset of the disease at a subsequent temporal juncture. These are invaluable in public health frameworks, wherein early interventions can be directed toward susceptible populations, possibly averting disease onset [6].

Risk Stratification Biomarkers: A notch higher in specificity, these biomarkers earmark individuals at augmented risk of disease manifestation. Their identification enables clinicians to prioritize interventions among high-risk cohorts.

Disease Screening Biomarkers: These markers emerge before the clinical manifestations of the disease, even before the first signs or symptoms, providing a preliminary indication of pathological changes, thus serving as early warning systems.

Diagnostic Biomarkers: These are the bulwarks of clinical practice, delineating the presence or absence of disease. Diagnostic biomarkers are pivotal in establishing clinical case definitions and facilitating systematic disease classifications.

Prognostic Biomarkers: With a forward-looking perspective, these biomarkers prognosticate the eventual trajectory of the disease, irrespective of therapeutic interventions. They are instrumental in forecasting potential disease recurrences or prognosticating the progression curve.

Predictive Biomarkers: These are quintessential in personalized medicine, enabling the demarcation of patients based on their predicted response to therapeutic regimens. Their primary function is to guide therapeutic decisions by distinguishing responders from non-responders within a diseased cohort [7].

Monitoring Biomarkers: Operating in a longitudinal capacity, these markers are evaluated serially to decipher the disease’s status under the influence of a specific therapeutic, device, or environmental agent [8].

In summation, elucidating and validating periodontal biomarkers signify a quantum leap in contemporary periodontics, promising transformative changes in diagnostic precision, prognostic accuracy, and therapeutic efficacy.

3. Role of biomarkers in gingival diseases

Gingival diseases are precursors to irrevocable degradation of periodontal tissues, manifesting the host’s immune inflammatory response that remains unchecked and unremedied against pathobionts. Diagnosing these diseases in their incipient stages and discerning active disease sites is paramount. Although the conventional methodology of periodontal screening and recording with a graduated periodontal probe remains the linchpin of diagnosis, its limitations are evident. For instance, full mouth gingival bleeding scores remain the archetypal metric to delineate a ‘Gingivitis Case.’ However, an over-reliance on bleeding scores poses challenges, given the low sensitivity of bleeding on probing.

Furthermore, probing inflamed or ulcerated tissues is not infrequently discomforting for both patient and clinician. The metrics denoting periodontal degradation, such as probing pocket depth and clinical attachment loss, only reflect antecedent periodontal destruction, offering no insights into contemporary disease dynamics. The quintessence of disease management lies in discerning susceptible individuals and sculpting patient-centric therapeutic strategies. A profound comprehension of the disease’s type, severity, and potential trajectory is instrumental for efficacious treatment planning and long-term management [7].

Current monitoring paradigms for gingival disease progression employ longitudinal evaluations encompassing bleeding indices, visual appraisals, and screenings to detect amplified probing pocket depth, attachment loss measures, and radiographic bone diminution. The intricate association between periodontal diseases and a panoply of systemic afflictions necessitates a precise quantification of periodontally inflamed surface areas and the systemic ramifications of the inflammatory burden engendered by these diseases at the individual patient echelon. A burgeoning field of research is gravitating toward discovering facile, economical, non-intrusive, and dependable biomarkers. The raison d’être for these biomarkers lies in identifying susceptible individuals, early diagnostic precision, disease progression assessment, oral inflammatory load quantification, therapeutic response evaluation, and the formulation of patient-tailored regimes for supportive periodontal therapy [9].

A contemporary shift in treatment paradigms has been observed, transitioning from traditional strategies where patients were mere passive care recipients to the more evolved ‘Precision Dental Medicine. This approach accentuates individualized diagnostic, therapeutic, and outcome metrics. The seminal 2017 AAP-EFP workshop underscored the exigency for the evolution and validation of minimally invasive diagnostic apparatus and the discernment of genetic predispositions or resistance factors [1, 10]. The Biomarker’s Definitions Working Group 2001 describes biomarkers as “alterations at the cellular, biochemical, molecular, or genetic levels which can highlight or differentiate between normal, abnormal, or specific biological processes [11]. Academically speaking, a biomarker is often understood as a uniquely characterized characteristic used to indicate typical biological processes, pathological processes, or responses to exposure or treatments [12].

4. Biomarkers in gingival and periodontal diseases

4.1 Microbial biomarkers in gingival diseases

Gingival diseases arise from the complex interplay between the host and the resident oral microbiota. A disruption in this delicate balance can precipitate disease onset and progression. Historically, the identification of microbes relied heavily on culture-based techniques. However, the advent of advanced molecular biology methods has marked a transition to more sophisticated molecular identification strategies, thereby increasing the precision of bacterial identification [13].

As gingival health deteriorates, there is a notable microbial shift. This involves transitioning from a mainly gram-positive environment in health to a predominantly gram-negative environment during gingivitis. Using advanced methods like 454-pyrosequencing, researchers have identified specific bacterial taxa associated with gingivitis, such as Fusobacterium nucleatum subsp. polymorphum and Lautropia sp. HOTA94, among others. Moreover, there has been a rigorous investigation into potential periodontal pathogens like Porhyromonas gingivalis and Aggregatibacter actinomycetemcomitans, to name a few [14].

The clinical relevance of microbial biomarkers in gingival diseases is manifold. These biomarkers can pinpoint pathogens linked to particular gingival diseases and guide treatments by determining antimicrobial susceptibilities [15]. Moreover, they offer insights into the ongoing activity of the disease, allowing for site-specific and individualized assessments. However, it is crucial to approach microbial biomarkers with caution. The current evidence has gaps, with some questioning the efficacy of these biomarkers in diagnosing and treating gingival diseases. The era of attributing gingival disease to a single microbial entity is fading. Contemporary research seeks to understand broader microbial community shifts during dysbiosis, considering microbial dynamics and host responses.

4.2 Inflammatory biomarkers in gingival Crevicular fluid (GCF)

Gingival diseases arise from complex interplays between host immune responses and microbial challenges. In this context, Gingival Crevicular Fluid (GCF) emerges as an invaluable diagnostic tool (Table 1). Originating as an exudate from gingival tissues, GCF provides a unique glimpse into the ongoing inflammatory processes at the site of periodontal disease [97]. Its composition, teeming with blood derivatives, tissue breakdown products, and microbial metabolites, underscores its diagnostic potential. Furthermore, the non-invasive method of GCF collection renders it a practical choice for clinicians.

Biomarkers	References
Acute-phase proteins; Lactoferrin, Transferrin,α2-Macroglobulin,α1-Proteinase inhibitor, C-reactive protein	Pradeep et al. [16]; Fujita et al. [17]; Kumar et al. [18]; Keles et al. [19]; Kinney et al. [20]; Kumari et al. [21]
Antibacterial Antibodies: IgG1, IgG2, IgG3, IgG4,IgM, IgA	Brajovic et al. [22]; Guentsch et al. [23]
Anti-Hsp70 (heat shock protein family A)	Takai et al. [24]
Aspartate aminotransferase	Shimada et al. [25]
Beta-N-acetyl-hexosaminidase	Buchmann et al. [26]
Calgranulin A (MRP-8)	Andersen et al. [27]
Calprotectin	Becerik et al. [28]; Kaner et al. [29]
Cathepsin G, D, B	Garg et al. [30]
CD14	Jin and Darveau. [31]
Chemerin	Doğan et al. [32]
Chitinase-3-like protein 1(YKL-40)	Kumar et al. [33]
Chondroitin 4-sulfate	Khongkhunthian et al. [34]
Chondroitin 6-sulfate	Khongkhunthian et al. [34]
Creatinine kinase	Nomura et al. [35]
Cystatins	Sharma et al. [36]
Cytokines: Interleukin - 1α, Interleukin - 1β, Interleukin -1ra, Interleukin-2,Interleukin - 6, Interleukin – 8	de Campos et al. [37]; Shaker and Ghallab [38]; Darabi et al. [39]; Fu et al. [40]; Lagdive et al. [41]; Shimada et al. [42]; Shivaprasad and Pradeep [43]; Keles et al. [19]
Cytokines: 1 L-4,1 L-17’ IFN-γ	Stadler et al. [44]
Dipeptidyl peptidases, Alkaline phosphatase, β-Glucuronidase, Stromyelysins, Lactate dehydrogenase; Arylsulfatase, Lysozyme, Dipeptidylpeptidase, Creatine kinase; Immunoglobulin-degrading enzymes; β-Glucuronidase, Trypsin - like Enzymes	Lamster and Ahlo [45]; Buduneli and Kinane. [46]
Elastase	Cox et al. [47]
Fibronectin fragments	Brajovic et al. [22]; Feghali and Grenier. [48]
Gingipain	Guentsch et al. [49]
Glycosaminoglycans (GAG’s):	Yan et al. [50]
Glycosidases	Soder et al. [51]
Hemoglobin β-chain peptides	Ngo et al. [52]; Kido et al. [53]
Hepatocyte growth factor	Anil et al. [54]
Human beta-defensins	Pereira et al. [55]
Hypoxia-inducible factor-1α	Zorina et al. [56]
Laminin	Emingil et al. [57]
Leptin	Karthikeyan & Pradeep [58]
Leukotriene B4	Pradeep et al. [59]
Lysophosphatidic acid (LPA)	Hashimura et al. [60]
Matrix metalloproteinase-1 (MMP-1); Matrix metalloproteinase-2 (MMP-2); Matrix metalloproteinase-3 (MMP-3); Matrix metalloproteinase-8 (MMP-8); Matrix metalloproteinase-9 (MMP-9); Matrix metalloproteinase-13 (MMP-13)	Sorsa et al. [61]; Tuter et al. [62]; Kushlinskii et al. [63]; Konopka et al. [64]; Khongkhunthian et al. [65]
MCP-4	Kumari et al. [21]
Melatonin	Ghallab et al. [66]
Monocyte chemoattractant protein-1 (MCP)	Anil et al. [67]; Gupta et al. [68]; Kumari et al. [21]
Monocyte chemoattractant protein-1(MCP-1)	Gupta et al. [68]
Myeloperoxidases	Buchmann et al. [26, 69]
Neopterin	Pradeep et al. [59]
Neurokinin A	Lundy et al. [70]
Neutral protease	Bader and Boyd. [71]
Osteocalcin	Becerik et al. [28]
Osteonectin, Hyaluronic acid, Hydroxyproline,	Buduneli and Kinane [46]
Osteopontin	Sharma and Pradeep. [72, 73]
PA inhibitor-2 (PAI-2)	Tuter et al. [74]
Pentraxin-3	Pradeep et al. [16]
Periostin	Kumaresan et al. [75]
Plasminogen	Yin et al. [76]
Plasminogen activator (PA)	Buduneli et al. [77]; Kardesler et al. [78]; Tuter et al. [74]
Platelet-Activating Factor	Chen et al. [79]
Progranulin	Priyanka et al., [80]
Prostaglandin E2	Buduneli et al. [81]
Pyridinoline crosslinks (ICTP)	Jepsen et al. [82]
RANTES (chemoattractant and activator of macrophages and lymphocytes)	Emingil et al. [83]
Receptor activator of nuclear factor-κB-ligand (RANK-L) and Osteoprotegerin (OPG)	Bostanci et al. [84]
Resistin	Gokhale et al. [85]
Substance P	Ozturk et al. [86]
Tissue inhibitor of MMP-1 (TIMP-1)	Kardesler et al. [87]; Marcaccini et al. [88]
Transforming growth factor-beta (TGF-β)	Kuru et al. [89]
Tumor necrosis factor α (TNF -a). Interferon α	Bastos et al. [90]
Vascular endothelial growth factor (VEGF)	Sakallioglu et al. [91]
Vasoactive intestinal peptide	Linden et al. [92]
Vaspin	Bozkurt Doğan et al. [93]
Visfatin	Pradeep et al. [94]
α1-Proteinase inhibitor	Nakamura-Minami et al. [95]
α2-Macroglobulin	Knofler et al. [96]

Table 1.

GCF biomarkers.

GCF’s cellular and molecular components paint a detailed picture of the local immune response. For instance, the cellular milieu of GCF includes neutrophils, which act as the vanguard against microbial invaders, epithelial cells, and various blood cells. On a molecular front, GCF is a reservoir of immunoglobulins (IgG, IgM, IgA), complement components, cytokines, and markers of tissue degradation, offering a comprehensive insight into the immune response at the periodontal site [98].

Delving deeper into its contents, GCF presents a suite of inflammatory biomarkers. Cytokines, the molecular sentinels of our immune system, have been extensively studied in GCF, with certain ones like IL-1β and IL-8 implicated in gingivitis. Their levels can offer insights into disease severity and therapeutic outcomes. Chemokines, another class of signaling molecules, are also represented, with molecules like MCP-1 correlating with disease severity. Furthermore, prostaglandins such as PGE2, synthesized by macrophages and fibroblasts, play pivotal roles in processes like collagen degradation, making them potential therapeutic targets [99]. Another fascinating aspect of GCF is the presence of adipokines, cytokines derived from fat cells. Notable adipokines such as resistin and leptin have associations with disease progression, with leptin potentially playing a protective role.

Matrix metalloproteinases (MMPs) in GCF, crucial for tissue remodeling, offer another layer of diagnostic potential. MMP-8, for instance, stands out as a reliable indicator of periodontal disease [100, 101]. Furthermore, GCF contains markers of bone remodeling, such as RANKL and OPG, which provide insights into alveolar bone turnover, a critical aspect of periodontal disease progression.

The broad spectrum of other biomolecules in GCF, ranging from enzymes like ALP and LDH to proteins like periostin and PTX-3, cements its importance in periodontal diagnostics. GCF is a treasure trove of biomarkers, presenting a promising avenue for disease diagnosis, therapeutic assessment, and prognosis in gingival diseases [102]. The intricate molecular pathways illuminated by studying GCF could spearhead targeted therapeutic strategies, heralding an era of precision medicine in periodontology. As our understanding deepens, future research promises to further refine the use of GCF in routine periodontal care.

4.3 Biomarkers in the saliva

As a diagnostic medium, saliva offers a promising and non-invasive approach to unearthing potential biomarkers in periodontal diseases [103]. Although it may not depict the intricate site-specific disease activity, saliva mirrors the overarching oral inflammatory scenario (Table 2).

Biomarkers	References
8-hydroxydeoxyguanosine (8-OHdG)	Sezer et al. [104]
Alanine aminopeptidase	Aemaimanan et al. [105]
Alkaline phosphatase (ALP):	Dabra and Singh. [106]
Aminotransferase	Nomura et al. [107]
Amylase	Sanchez et al. [108]
Arginase	Pereira et al. [109]
Ascorbate	Sculley and Langley-Evans Sculley and Langley-Evans. [110]
Calcium (Ca):	Kiss et al. [111]
Chitinase	Van Steijn et al. [112, 113]
Cortisol	Refulio et al. [114]
C-reactive protein	Shojaee et al. [115]
Cystatins C, S, A, SN	van Gils et al. [116]
Cytokines, IL1-β	Kim et al. [117]
Dipeptidyl peptidase	Aemaimanan et al. [105]
Elastase	Pauletto et al. [118]
Esterase	Bimstein et al. [119]
Hemoglobin	Ito et al. [120]
Hepatocyte growth factor – HGF	Lonn et al. [121]
Immunoglobulins (G, A, M, SIgA)	Olayanju et al. [122]
Interleukin 6 (IL-6)	Costa et al. [123]
Lactate dehydrogenase (LDH)	Nomura et al. [107]
Lactoferrin	Rocha Dde et al. [124]
Lysozyme	Surna et al. [125]
Matrix metalloproteinase-1(MMP-1)	Yildirim et al. [126]
Matrix metalloproteinase-8 (MP-8)	Yildirim et al. [126]
MCP-1	Nisha et al. [127]
Melatonin	Almughrabi et al. [128]
MMP 3	Kim [129]
Myeloperoxidase	Meschiari et al. [130]
Neopterin	Ozmeric et al. [131]
Nitric oxide (NO)	Sundar et al. [132]
Osteoprotegerin	Tabari et al. [133]
Platelet activating factor (PAF)	McManus and Pinckard, [134]
S100A8	Kim et al. [135]
Tissue inhibitor of matrix metalloproteinase (TIMP)	Isaza-Guzman et al. [136]
TNF-α	Kibune et al. [137]
Urate	Sculley and Langley-Evans. [110]
α-1-antitrypsin, Keratin, Complement C3; Fibronectin, Albumin, Epidermal growth factor (EGF), Vascular endothelial growth factor (VEGF)	Nomura et al. [107]
α-2-macroglobulin	Ozmeric et al. [131]
β-Glucuronidase	Lamster et al. [138]

Table 2.

Salivary biomarkers in periodontal diseases.

Host Inflammatory Mediators form the crux of the salivary biomarkers. MMP-8 is a crucial biomarker, aligning closely with periodontal disease severity, progression, and therapeutic outcomes [100]. Other members, such as MMPs-2,3,9,13, too, hold their ground in this context. Neutrophilic Mediators, with members like Cathepsin G, elastase, lysozyme, lactoferrin, and myeloperoxidase, directly associate with escalating periodontal disease severity [139]. Additionally, cytokines such as IL-1β, TNF-α, and IL-6 resonate with the gravity of periodontal ailments and their response to treatments.

Venturing into Chemotactic Cytokines, MCPs, namely MCP-1 (CCL2) and MCP-4 (CCL13), see a marked rise with the advancing periodontal tissue damage but showcase a decline post-intervention. MIP1-α, too, holds promise as a salivary biomarker [140]. One should also note that the cell surface molecule CD 44 plays a pivotal role in neutrophil recruitment and presents elevated levels in periodontal afflictions.

Growth Factors add another dimension. The Hepatocyte Growth Factor, for instance, is on the radar as a prospective biomarker gauging periodontal disease activity. Several other growth factors, such as platelet-activating factor, platelet-derived growth factor, TGF-α, and TGF-β, have been detected in saliva, underscoring their potential significance [141].

The realm of antioxidants and neuropeptides offers melatonin, celebrated for its antioxidant attributes. Remarkably, its levels wane in the face of periodontal diseases but recover post-treatment. Neuropeptides, specifically VIP and NPY, demonstrate connections with bleeding scores among periodontal patients [142].

Adipokines, notably visfatin, with its heightened levels, align with periodontal disorders, yet these levels recede after therapeutic measures. Another adipokine, chemerin, is currently scrutinized for its diagnostic prowess [143].

Diving into Biochemical Markers, enzymes such as Aspartate aminotransferase, alkaline phosphate, and lactate dehydrogenase manifest heightened salivary levels in periodontal ailments. Various other enzymes, from Alpha-glucosidase to trypsin, have also been examined in this context [144].

Lastly, the Immunoglobulins domain brings forth IgA, IgG, and IgM titers, which have been evaluated to discern their dynamics in periodontal diseases [145].

Saliva is a treasure trove of information, offering a panoramic view of periodontal diseases. Its components can adeptly signal disease severity, evolution, and the efficacy of treatments, cementing its role in periodontal diagnostics. Yet, a clinician’s proficiency in harnessing these biomarkers remains paramount for optimal patient care.

4.4 Genetic and molecular biomarkers in periodontal diseases

Genetic predisposition plays a pivotal role in determining individual susceptibility to gingival diseases. Biomolecular markers, particularly genetic markers, map inherited differences among subjects to their DNA profile. The TNF-α and IL-1 gene polymorphisms have been shown to influence levels of inflammatory mediators in biofluids. Additionally, studies have noted differential responses in individuals to plaque accumulation, suggesting that genetic control primarily determines this [146, 147].

Interestingly, children with Down’s syndrome developed rapid and profound gingival inflammation upon exposure to dental plaque over 21 days. Twin studies have further reinforced the idea of genetic control over clinical expression patterns in periodontal diseases, with a significant 82% of population variance in gingivitis attributed to genetic susceptibility [148]. Dominant pathways of gene expression, particularly immune response, were identified in gingival biopsy samples from an experimental gingivitis study [149].

Modern genetic research integrates whole-genomic arrays with a specific mapping of candidate genes. These genes, identified for their biological significance in disease etiopathogenesis or supported by genomic studies, are examined for single nucleotide polymorphisms (SNP) that could indicate susceptibility.

4.5 Key points

• Polymorphisms at the promoter region of IL-6, IL-10, and IL-18 play crucial roles in gingival disease etiology [150].

• A potential risk factor for children’s gingival diseases is the interaction between IL-18 and MMP-9 genes.

• IL-1Ra gene polymorphisms indicate genetic susceptibility to gingival diseases.

4.6 Genes and periodontal disease susceptibility

Several candidate genes have been under investigation for their association with periodontal disease. Some key candidates include:

• IL-1: A pro-inflammatory cytokine, divided into subfractions IL-1α and IL-1β, encoded by the IL-1 gene cluster at locus 2q13–21. IL-1α acts as an ‘alarming from necrotic cells, while IL-1β is crucial for innate immune response [151, 152, 153].

• TNF-α: Another pro-inflammatory cytokine encoded at locus 6p21.3. Research has been inconclusive regarding its association with periodontal disease [154].

• IL-6 and IL-10: These cytokines are in MMP activation and inflammation regulation, respectively. Their SNPs have been explored as potential genetic markers.

• IFN-γ and TGF-β encoded on different chromosomes have been researched for their SNP’s potential relation to periodontal diseases.

• MMP and TIMP: Key proteins in matrix degradation and inhibition, respectively, with their polymorphisms evaluated as potential biomarkers. No significant associations have been found.

• Vitamin D: Significant in bone homeostasis and immune function, with polymorphisms in its receptor gene explored for their potential association with periodontal disease [154].

4.7 Proteomic and Metabolomic biomarkers

Proteomic studies provide a snapshot of the protein array within specific biological contexts. Periodontal research typically derives these from GCF, saliva, serum, and tissues from gingival regions using LC-MS/MS tools. Key findings include upregulated proteins like S100 proteins, MMP-8, and MMP-9 and downregulated ones like cystatins and cytokeratin [155]. Metabolomics, on the other hand, reflects enzymatic and metabolic pathways. Identified metabolites in periodontal diseases include products of microbial metabolism, host immune responses, and tissue degradation. Studies have noted a range of potential metabolite biomarkers from various sources like saliva and GCF [156].

4.8 Systemic inflammatory markers

The relationship between oral and systemic inflammation provides the rationale for exploring systemic inflammatory markers in periodontal inflammation. Although C-reactive protein is the most studied marker, its specificity for periodontal inflammation remains debatable [157]. Understanding genetic, proteomic, and metabolomic markers can revolutionize our approach to diagnosing and treating periodontal diseases. As we unravel these biomarkers, there is hope for more targeted and personalized treatments in periodontal care.

5. Clinical applications of gingival disease biomarkers

5.1 Diagnosis and screening

Early Detection: The significance of early detection in gingival diseases is analogous to its importance in systemic conditions like cancer. Just as detecting cancer at an initial stage enhances survival rates, identifying gingival diseases early can prevent irreversible tissue damage and bone loss. Biomarkers, sensitive indicators of molecular and cellular changes, are perfectly poised to detect diseases before clinical signs and symptoms appear [158]. Utilizing these markers leads to pre-symptomatic interventions involving less invasive procedures and yielding better outcomes.

Differential Diagnosis: The oral cavity presents many conditions, many of which can manifest with similar clinical signs. Biomarkers can help navigate this complexity. For instance, the elevated levels of specific inflammatory cytokines might suggest an active gingival disease, whereas other biomarkers might indicate non-inflammatory conditions [15]. This precision ensures that patients are not subjected to unnecessary or harmful treatments.

Risk Prediction: Traditionally, patient history and clinical assessments were the primary tools to gauge disease risk. A molecular understanding of a patient’s vulnerability to gingival diseases is possible with biomarkers. It is analogous to using cholesterol levels to predict heart disease risk. Such predictions can lead to proactive strategies, such as more frequent dental check-ups for high-risk individuals [159].

Site-Specific Assessment: Gingival diseases do not always affect the oral cavity uniformly. Some sites can be more susceptible to plaque retention, occlusion, or tooth anatomy. GCF biomarkers offer a glimpse into the molecular health of specific gingival sites. Although their utility in site-specific diagnosis is still debated, they undeniably provide another layer of diagnostic detail [160].

5.2 Prognosis and disease progression

Disease Severity: Biomarkers can act as quantitative measures of disease intensity. For example, elevated matrix metalloproteinase (MMP) levels in the GCF might correlate with increased tissue destruction [161]. Such molecular insights can help clinicians decide between conservative or aggressive treatment approaches.

Disease Progression: Biomarkers can be dynamic indicators that chart the disease’s progression or regression. For instance, a decline in inflammatory cytokines post-treatment might suggest a favorable response, while a surge could indicate disease exacerbation [162]. Such real-time feedback can inform the need for treatment modifications.

5.3 Treatment monitoring

Treatment Response: Imagine if clinicians could predict how a patient would respond to treatment before initiating it. Specific biomarkers can provide this foresight. Tracking these markers during treatment can also give clinicians a sense of whether the chosen intervention works at the molecular level, even if overt clinical signs have yet to change [146].

Therapeutic Efficacy: Biomarkers can be likened to gauges that help fine-tune treatments. For instance, persistent elevation of certain biomarkers post-treatment might suggest the need for a more extended therapeutic course or the addition of adjunctive treatments [163].

5.4 Risk assessment and personalized medicine

Personalized Periodontics: Everyone has a unique genetic, microbial, and environmental footprint. Biomarkers provide a window into these individualized factors, enabling tailored treatments. For instance, genetic biomarkers might suggest a hereditary susceptibility to aggressive periodontitis, guiding therapeutic and preventive strategies [164].

Preventive Care: Biomarkers can shift the treatment paradigm from reactive to proactive. Recognizing an individual’s heightened susceptibility to gingival diseases can lead to early interventions, which can be as simple as lifestyle modifications or as specific as targeted pharmacological prophylaxis.

Biomolecular Insights: The advent of omics technologies (genomics, proteomics, microbiomics) has expanded the horizon of potential biomarkers. Understanding the host-microbial interactions at the molecular level can unravel novel therapeutic targets and even pave the way for microbiome-modulating therapies.

5.5 Precision periodontics

Precision medicine is reshaping healthcare. Within periodontics, this translates to treatments based not just on clinical presentation but also on a patient’s genetic, microbial, and biomolecular profile. This evolution ensures that each patient receives the most effective, least invasive, cost-effective care tailored to them. The vast potential of biomarkers in periodontics is gradually coming to fruition [165]. As research progresses and more biomarkers are identified and validated, the future of periodontal care looks increasingly personalized, precise, and promising.

6. Methods for biomarker detection and analysis in gingival diseases

6.1 Collection methods

Gingival Crevicular Fluid (GCF) Collection: Absorbent paper strips or microcapillary tubes: The ease of collecting GCF using these tools makes them popular. Paper strips are typically made of cellulose material, ensuring effective fluid absorption [161]. Once inserted into the gingival sulcus, these strips wick up the fluid. It is important to ensure that blood contamination does not occur, as it can dilute and alter the sample’s composition. Microcapillary tubes, on the other hand, use capillary action to draw in the GCF. The volume collected is often directly read off the calibrated tubes [166].

Salivary Collection: Passive drool: This method involves asking the patient to pool saliva at the bottom of the mouth and then drool it into a collection vessel. While simple, it ensures a clean, uncontaminated sample ideal for various analyses [167].

Salivary swabs or sponges: These swabs, typically made of polyurethane foam or other absorbent materials, are placed in the mouth, allowing them to soak up saliva [168]. They are subsequently centrifuged or squeezed to extract the salivary sample.

6.2 Biochemical analyses

Enzyme-linked immunosorbent assay (ELISA): ELISA operates on an antigen-antibody reaction. When the biomarker (antigen) of interest is present, it will bind to specific antibodies, either directly attached to a plate or added during a test [169]. A secondary antibody linked to an enzyme is added; if binding occurs, a substrate will produce a color change proportional to the biomarker concentration.

Polymerase Chain Reaction (PCR): PCR is a molecular biology technique that amplifies targeted DNA sequences. In the context of gingival diseases, it’s essential for detecting specific bacterial pathogens [170]. Real-time PCR (qPCR) offers the advantage of quantifying the amount of DNA, giving insights into the bacterial load.

Mass Spectrometry: This technique identifies and quantifies molecules by measuring their mass-to-charge ratio. Protein analysis in GCF or saliva offers high sensitivity and specificity [161].

7. Advanced genomic and proteomic techniques

Microarrays are arrays where DNA probes are attached to a solid surface. When a complementary DNA or RNA sample has flowed over the array, it binds to the probes, allowing for the simultaneous analysis of thousands of sequences [171]. It can detect gene expression patterns, potentially identifying disease susceptibility or progression genes.

Next-Generation Sequencing (NGS): Unlike traditional sequencing methods that analyze one DNA fragment at a time, NGS can simultaneously sequence millions of fragments, giving a comprehensive genetic overview. It is invaluable in analyzing the complex oral microbiota involved in gingival diseases [172].

Liquid Chromatography-Mass Spectrometry (LC-MS): LC-MS combines liquid chromatography’s physical separation capabilities with mass spectrometry’s mass analysis capabilities [173]. It is particularly effective for proteomics, allowing for detailed sample protein profiling.

7.1 Imaging techniques

Immunohistochemistry: This technique uses antibodies tagged with a detectable marker, often a colored dye or a fluorescent compound, to visualize specific proteins in tissue sections. Analyzing tissue samples from gingival pockets allows localizing and quantifying protein biomarkers directly within the disease site [15].

Flow Cytometry: This method analyzes the light-scattering properties of cells (or particles) in a fluid as they pass through a laser beam. Detectors capture the scattered light, allowing for the identification and quantification of cellular biomarkers. In gingival disease research, it is especially useful in analyzing inflammatory cells in GCF [174].

With the advancement of these methods, the realm of periodontal research and diagnosis is rapidly evolving. These techniques allow for precise identification and quantification of biomarkers and a deeper understanding of disease mechanisms, paving the way for better therapeutic strategies.

8. Novel biomarkers and emerging research

As periodontal research advances, numerous novel biomarkers are being identified and explored for their potential in diagnosing, prognosis, and managing gingival diseases [15]. These developments shift the traditional understanding of gingival diseases toward more precise and individualized care.

Microbiome-Associated Biomarkers: Microbiome-associated biomarkers are gaining traction in periodontal research, offering a nuanced lens through which the intricate dynamics of the oral microbial ecosystem can be understood [175]. The very essence of the oral microbiome is its intricate mosaic of microorganisms, each playing a pivotal role in maintaining oral health. Yet, disturbances in this delicate balance, known as dysbiotic shifts, can herald the onset or progression of periodontal diseases. Researchers have harnessed the power of Next-Generation Sequencing (NGS) to decipher this intricate microbial tapestry, focusing mainly on the bacterial 16S rRNA genes [176]. This approach furnishes a panoramic view of bacterial communities, unearthing the breadth of microbial diversity. Yet, the path to crystallizing these insights into definitive predictive microbial biomarkers poses formidable challenges, underscoring the complexity of the task at hand and the intricacies of the oral microbiome itself.

Epigenetic Biomarkers: Epigenetic biomarkers represent a burgeoning frontier to enhance our diagnostic and prognostic capabilities in periodontal diseases [177]. Fundamentally, epigenetics pertains to the heritable shifts in gene activity that occur independent of alterations in the DNA sequence itself. The allure of epigenetic markers lies in their potential to furnish invaluable insights into the onset, trajectory, and even the therapeutic responses of diseases. At the heart of contemporary epigenetic research are phenomena like DNA methylation and histone modifications, notably acetylation and methylation. For instance, the spotlight has been cast on genes such as TNF, IFNG, PTGS2, SOCS1, IL-6, and IL-6R, assessing their pertinence to gingivitis and periodontitis [178]. Moreover, the scientific community is progressively venturing into the intricate tapestry of genome-wide methylation patterns, aiming to unravel the deeper epigenetic underpinnings of periodontal afflictions.

MicroRNAs (miRNAs): MicroRNAs (miRNAs) stand as an intriguing facet of biomarker research, serving as diminutive, non-coding RNA fragments that are pivotal in regulating gene expression [179]. Intriguingly, miRNAs are deeply embedded in myriad biological pathways, most notably in inflammatory processes, positioning them as prospective biomarkers for periodontal diseases. Many miRNAs, including miR-146a, miR-20a, and miR-32, have been thrust into the limelight for their potential diagnostic implications [180]. While their inherent stability underscores their promise as reliable biomarkers, the road to their widespread clinical adoption is challenging. Notably, the diversity in research findings and the financial constraints associated with their deployment remain pertinent challenges. As such, while miRNAs hold significant potential, a more concerted effort is required to streamline their integration into routine clinical settings.

Transcriptomics: Transcriptomics, an innovative approach in biomarker research, centers around cataloging the entire suite of RNA transcripts generated by an organism’s genome [181]. Within the scope of periodontal diseases, transcriptomics plays an instrumental role. It pinpoints alterations in gene expression profiles and sheds light on the underlying mechanisms driving the disease’s progression. Doing so provides a roadmap for researchers and clinicians to identify potential therapeutic targets. In essence, including transcriptomic analyses in periodontal research offers a more granular insight into disease pathways, paving the way for personalized and targeted treatment strategies [182].

Metagenomics: Metagenomics holds a pivotal role in understanding the microbial landscape of the oral environment. Metagenomics offers a holistic snapshot of the microbial community by analyzing the collective DNA from the microorganisms present in each sample, highlighting their genetic compositions and potential functions [183]. As noted by third-party researchers, one of the primary strengths of this approach is its ability to bypass the traditional and often time-consuming methods of individual microbial culturing. Instead, it provides an undiluted, comprehensive view of microbial community dynamics. As external experts in the field emphasized, this shift in methodology could pave the way for unprecedented insights into gingival diseases, potentially revolutionizing diagnostic procedures, risk assessments, and therapeutic strategies [184]. As metagenomic techniques continue to refine and become more accessible, many in the scientific community believe they stand at the precipice of a new era in periodontal research and treatment.

9. Challenges and future directions

The pursuit of definitive biomarkers for periodontal diseases is both exciting and challenging. The potential rewards of early diagnosis, risk stratification, and personalized therapeutic interventions make it a vibrant area of research. However, there are significant challenges to be surmounted.

Standardization and Validation: The robustness of biomarkers in clinical applications hinges on rigorous standardization and validation. At the crux of this is the biomarker’s sensitivity and specificity, which, together with its positive and negative predictive values, dictate its reliability and efficacy. The Receiver Operating Characteristic (ROC) Curve is an indispensable tool in this assessment. Researchers can determine a biomarker’s discriminative prowess by analyzing the ROC and the Area under the curve (AUC) values [185]. Beyond these metrics, a thorough validation process spanning analytical and clinical domains is paramount. This ensures the biomarker’s consistent performance across varied settings and populations and its apt reflection of the underlying disease pathology.

Biomarker Panels and Algorithms: In the context of periodontal diseases, the intricate etiology necessitates a multi-dimensional approach to diagnosis and prognosis. Rather than relying on a singular biomarker, the amalgamation of clinical, microbial, and immunological markers offers a nuanced and comprehensive insight into the disease’s manifestation and progression [146]. Algorithms crafted for interpreting such data from an ensemble of biomarkers hold the promise of superior diagnostic precision. This perspective is underscored by investigative trials highlighting the heightened sensitivity and specificity yielded by biomarker panels. For instance, the convergence of markers such as Porphyromonas gingivalis, Taneralla forsythia, and MMP-8 indicates severe periodontitis [186]. The collective strength of such biomarkers, integrated with clinical parameters, has markedly enhanced the sensitivity in predicting disease progression [187].

Longitudinal Studies and Predictive Models: Longitudinal studies play a pivotal role in periodontal disease research by offering a dynamic perspective on disease progression. By observing patients over extended periods, these studies facilitate the identification of biomarkers that serve dual roles: detecting the current state of the disease and forecasting its future trajectory [188]. Beyond mere identification, the evolution of these biomarkers in response to therapeutic interventions can be charted, thereby providing invaluable insights into the efficacy of treatments. Consequently, these studies deepen our understanding of disease pathways and refine our approach to patient-specific treatments, ensuring that current conditions and potential future outcomes inform therapeutic decisions.

Point-of-Care and Wearable Technologies: The future of periodontal diagnostics is rapidly shifting toward more accessible, point-of-care, and wearable technologies. The modern emphasis on immediate, on-site diagnostic results is paving the way for innovative technologies like biosensors and micro-electro-mechanical systems (MEMS) [189]. These tools, capable of decentralizing the diagnostic processes, are the harbingers of a new era in patient care. Integrating diagnostics with daily life, such as embedding biosensors in mouthguards or enamel, holds the potential to provide real-time data. This immediate feedback facilitates prompt decision-making, ensuring timely interventions tailored to individual patient needs.

Furthermore, the rise of ‘Lab on a Chip’ (LOC) technology is set to usher in a paradigm shift [190]. By combining laboratory precision with portability, LOC enables accurate diagnostics right at the patient’s side, minimizing delays and maximizing efficiency. As we stand on the brink of these technological advancements, it becomes evident that biomarker research in gingival diseases is evolving and redefining how we approach periodontal conditions. The confluence of biomarker panels, wearable diagnostic tools, and predictive algorithms heralds a future where treatments are more individualized, timely interventions, and optimized outcomes. However, realizing this potential requires sustained research efforts, collaborative approaches across disciplines, and a relentless drive to push the boundaries of technological possibilities. The overarching aim remains unwavering: to elevate patient care standards, reduce disease impact, and ensure optimal therapeutic outcomes in periodontology.

10. Conclusion

Biomarkers have emerged as invaluable tools in gingival diseases, offering insights far beyond traditional clinical observations. Their potential spans early detection, prognosis, treatment monitoring, and tailored therapeutic approaches, indicating a significant shift toward precision periodontics. Advanced techniques, ranging from ELISA to Next-Generation Sequencing, have expanded the toolkit for biomarker detection, ensuring enhanced specificity and sensitivity. The non-invasive diagnostic potential of Saliva and Gingival Crevicular Fluid (GCF) promises more accessible screenings and regular monitoring, poised to revolutionize early interventions. As periodontal research delves deeper into microbiome-associated biomarkers, epigenetics, transcriptomics, and metagenomics, new avenues for more specific and reliable indicators of gingival health and disease are unveiled. However, the journey to integrating these findings into clinical practice is fraught with challenges, particularly the need for rigorous standardization and validation. Amalgamating multiple biomarkers could redefine diagnostic accuracy, while longitudinal studies may lay the foundation for robust predictive models. The emergence of point-of-care technologies, wearable devices, and MEMS heralds an era of real-time monitoring and detection. These advancements underscore the essence of collaborative efforts in the scientific community. At this juncture, integrating technology and biology suggests a future where gingival disease management is increasingly predictive, personalized, and efficient.

Conflict of interest

The authors declare no conflict of interest.