Microbiota of Saliva: A Non-invasive Diagnostic Tool

Abstract

Potential of salivary microbiota as a non-invasive diagnostic tool for various diseases are explained in the present review. Traditional diagnostic methods rely on blood, which has limitations in terms of collection and biomarker specificity. We discuss the concept of normal flora and how disruptions in oral microbiota can be indicative of diseases. Saliva, harboring a diverse microbial community, offers promise as a diagnostic biomarker source for oral and non-oral conditions. We delve into the role of microbial dysbiosis in disease pathogenesis and the prospects of using biological indicators like dysbiosis for diagnosis, prediction, and monitoring. This review also emphasizes the significance of saliva microbiota in advancing early disease detection and timely intervention. We addressed the following research question and objectives: Can the microbiota of saliva serve as a non-invasive diagnostic tool for the early detection and monitoring of both oral and non-oral diseases? To achieve this, we will explore the normal flora of microorganisms in the oral cavity, the impact of microbial dysbiosis, and the potential of using specific pathogenic microorganisms as biomarkers. Additionally, we will investigate the correlation between oral and non-oral diseases by analyzing total saliva or site-specific dental biofilms for signs of symbiosis or dysbiosis. This research seeks to contribute valuable insights into the development of a non-invasive diagnostic approach with broad applications in healthcare.

Introduction

In the human body, microorganisms thrive. The human microbiota is vital to maintaining normal health [1]. The human microbiota differs significantly based on its location in the body. The adult human male contains about 40 trillion bacteria and 30 trillion cells. Despite ongoing transmission across several bodily locations, each organ of the human body has its own characteristic resident microbial flora, such as the mouth, gut, colon, and skin.

Figure 1 illustrates the vital function of the microbial community in regulating physiological, metabolic, and immune processes within the human body, and a similar correlation has been observed between the oral microflora and salivary bacteria.

Fig. 1.

The vital role of microbial population in the human body

Crucial role of microflora in maintaining human health and physiology can be explained by its function as a microbial barrier and its production of antimicrobials [2]. Microflora in the oral cavity acts as a barrier against potential pathogens by competing for nutrients and space, and also produce antimicrobials such as bacteriocins that inhibit the growth of harmful bacteria and help regulate oral physiology. Secretory immunoglobulin A in the saliva can prevent microorganisms from adhering to the oral mucosa and teeth, as well as prevent foreign antigens from penetrating the mucosa, playing a crucial role in the first line of defence. Although secretory immunoglobulin A has anti-adherence capabilities against pathogens, its protective effect against indigenous microbiota is still controversial [3]. The presence of secretory immunoglobulin A, lysozyme, lactoferrin, and peroxidase in saliva enhances its immunological function. Lysozyme helps break down bacterial cell walls by hydrolyzing glycosidic linkages [4], and peroxidase neutralizes the harmful effects of hydrogen peroxide [5] produced by oral microorganisms. Lactoferrin, an iron-binding glycoprotein, inhibits the growth of pathogens by chelating with irons in the environment or by agglutinating the pathogens [3]. Histatins and cystatins, also present in saliva, inhibit co-aggregation of pathogens and prevent the production of proteases by suspected periodontopathogens [6, 7]. However, the immunological ability of saliva is not sufficient to reach the gingival crevice, and the cellular and humoral components of blood must travel through the gingival junction epithelium to reach the crevice.

Several species of bacteria colonize the oral cavity, and on exposed surfaces, multi-species biofilms are formed by the oral microbiota [8]. The second largest habitat for a diverse ecosystem in the human body is the oral cavity [9]. Several bacterial species can only attach to specific areas of the mouth cavity due to a biochemical connection between receptors on the host cell surface and adhesion molecules on the cell surface.

This biochemical interaction is also governed by the pioneer germs which are the first to colonize and other species can unite around a pioneer organism through co-aggregation or adhesion. The development of oral and systemic diseases may be influenced by early oral colonization and the development of a healthy microbiome [10].

Mouth cavity is always covered in saliva, which provides the ideal conditions for bacterial adherence [11]. There are many functions performed by saliva, including antibacterial, antiviral, and antifungal activity, which is a complex mixture of physicochemical and biological components. In addition to buffering plate acid capacity, saliva also contains enzymes essential for digestion, such as amylase, protease, and nuclease.

The organic components of saliva include amino acids, cystatins, defensins, statherins, carbonic anhydrases, peroxidases, lactoferrin, and mucins. Additionally, the distinctive makeup of saliva contains a subset of bacteria that can be used as a biomarker for the detection of both oral and extraoral disorders [12]. Figure 2 summarizes the distinct elements of saliva.

Fig. 2.

The distinguishing components of the saliva

Considering the various components such as bacterial subsets or bio-transformed chemicals which can be directly as a biomarker for the disease diagnostic purpose. Currently such diagnostic tools are not available and hence, in the present review authors have tried to attempt comprehensive and critical insights of human salivary microbiota and its possible implementations to being the non-invasive diagnostic tools for detection of various human diseases.

Factors Affecting Compositional Variability of Saliva

A majority of the microbiota in the saliva is associated with bacteria adhering to epithelial cells shed from mucosal surfaces, similar to those found in the oral mucosa, throat, and tonsils [13]. Furthermore, saliva contains microorganisms originating from supra- and subgingival environments. As a result, variables affecting microbial colonization in these niches indirectly affect saliva microbial composition.

Several factors influence salivary bacterial growth, including temperature, pH, oxidation–reduction potential, diet, age, hormonal changes, salivary flow, stress, oral hygiene, oral diseases, genetic factors, oral structure, and antimicrobial compounds administered in diseased conditions [14]. In addition to coaggregation, antibacterial compounds developed by the pioneer microorganism, and food chains, other variables also influence bacterial diversity in saliva. Several factors can alter salivary microbiota composition, including smoking, oral contraceptives, malnutrition, partial or full dentures, and the host microenvironment. To maintain a bacterial population balance, each component of a specific oral environment affects salivary microbe development.

Saliva composition is also determined by a proteinaceous film, called an acquired pellicle, composed of proline-rich proteins, albumin, lysozyme, glycoproteins such as lactoferrin, IgA, IgG, amylase, phosphoproteins, and lipids [15]. As a result of salivary flow removing bacteria from the pellicle with low affinity, pioneer bacteria rapidly multiply, forming microcolonies in an extracellular matrix containing both bacteria and host components after initial colonization. As a result of altering the environment, pioneers enable other bacterial species to colonize the region, such as Veillonella and Haemophilus [16, 17].

Salivary Microbiome Composition

There have been several studies and reports that demonstrate the importance of the human microbiome for overall health benefits. It has also been shown that oral disorders can be correlated with bacterial species [18]. For studies of the oral microbiome, saliva is used as both a component and a source of data. Saliva can be collected easily and non-invasively, making it an appealing research area [19, 20].

Saliva fluid covers the surface of the mouth cavity, containing proteins and enzymes that are vital biomolecules. Several salivary components play a major role in the homeostasis of the mouth, which is why they have antimicrobial properties. Table 1 summarizes these antimicrobial properties.

Table 1.

Antimicrobial role of different salivary components

Sr. No	Component	Function	Target
1	Water	Oral clearance	Elimination of microorganisms, dietary sugars and acids by swallowing
2	Salivary proteins	Formation of the acquired enamel and mucosal pellicle	Promoting adhesion of a number of microorganisms but also inhibiting adhesion and colonization of others and potentially pathogenic microorganisms
3	Glycoproteins, lipids	Nutritional source	Providing nutrition for several microorganisms to keep the balance oral microbiome
4	Amylase	Antibacterial Hydrolysis of starch	Certain streptococci bind specially to amylase Providing nutrition for several bacteria
5	Lysozyme	Antibacterial Antifungal Antiviral	Hydrolysis of the polysaccharide layer of the gram-positive bacterial cell wall Gram-positive bacteria, Candida, virus
6	Immunoglobulin, especially sIgA	Antimicrobial	Inhibition of microbial adhesion Enhancement of phagocytosis Aggregation of microorganisms in interactions with other salivary protein

Microbial Diversity in Salivary Microbiome

A healthy individual has 250–300 resident strains of the microbiome in their saliva, which contains more than 700 bacterial species. Among the important bacterial groups found in saliva are Firmicutes, Proteobacteria, Bacteroidetes, Fusobacterium, Actinobacteria, and the Unclassified group. Lactobacilli are a major group of salivary microorganisms that are commonly found in the gastrointestinal tract and have probiotic properties. Generally, oral diseases are not caused directly by bacteria in a planktonic stage in saliva, but pathogenic bacteria can be transmitted intraorally through them. Several diseases are linked to abnormal oral microbiota caused by the colonization of pathogenic bacteria in the throat and saliva, including pneumonia, periodontitis, acute post-infectious glomerulonephritis, rheumatic fever, septicemia, cardiac health, preterm birth, and cognitive impairment. Using such a linkage can assist in the diagnosis of oral and non-oral diseases.

Obesity

There are numerous diseases caused by obesity, including cancer, diabetes, coronary heart disease, asthma, hypertension, and others. There are a number of factors that contribute to obesity, including unbalanced calorie intake, low caloric expenditure, gene expression, stress, and endocrine anomalies. Also, obesity is now associated with a microbiota that is associated with an individual. Energy and food supply can be altered by microbiota to alter metabolic phenotypes.

It was reported in a recent study by Bombin et al. [21] that changes in obesity status at the population level are more likely to affect salivary microbiota composition than feces. Researchers have found elevated bacterial expression of genes associated with immunological disorders in obese individuals. It is therefore possible to develop innovative preventive and therapeutic approaches to obesity if we understand the mechanisms leading to it better.

A study by Raju et al. [22] of Finnish children aged 11–14 found that the saliva of overweight and obese people showed lower levels of Acinetobacter, Alysiella, Simonsiella, Xylanibacter, and Acidovora at the genus level, while the saliva of underweight people showed lower levels of Enteric bacteria, Kingella, and Anaerovorax. This study demonstrates how changes in salivary microbiota are linked to changes in body weight and may be used to identify pathways that lead to obesity. Based on the findings of Wu et al. [23], obese individuals differ significantly from healthy adults in terms of microbial diversity and structure in their salivary microbiome. It appears that people who are overweight or obese are more likely to develop oral diseases due to alterations in their salivary microbiome. It was found that both adults with obesity and controls had significantly lower bacterial diversity and richness in their salivary microbiomes. The obese group had a higher prevalence of Prevotella, Granulicatella, Peptostreptococcus, Solobacterium, Catonella, and Mogibacterium, but a considerably lower prevalence of Haemophilus, Corynebacterium, Capnocytophaga, and Staphylococcus. Several studies have demonstrated that obesity is correlated with a higher Firmicute abundance and a lower Bacteroidetes abundance.

The bidirectional relationship between several oral disorders and systemic disease is particularly strong when it comes to poor oral hygiene. According to Wu Y., et al. [23], individuals with normal body weight had different microbial diversity, structure, and composition than obese individuals. Obesity is often caused by changes in salivary microbial diversity. A study by Zeigler et al. [24] revealed that phylogenetic diversity was higher in obese adolescent oral subgingival biofilms due to increased cellular abundance. It was found that salivary samples from obese and normal-weight individuals differed biologically and microbiologically.

The researchers warned that periodontal disease may have skewed the findings, but more phylogenetic diversity was found in the salivary microorganisms of obese Japanese people. People with obesity have higher concentrations of P. gingivalis, T. forsythia, and Fusobacterium nuclatum in their saliva, regardless of their diabetes status [25–28].

Diabetes

It has been shown that type 2 diabetes and periodontitis have a strong bidirectional relationship, as evidenced by changes in saliva bacteria. Periodontitis and type 2 diabetes are also linked to poor oral health and systemic diseases [29]. Researchers have examined the salivary microbiomes of diabetes patients using next-generation sequencing in numerous studies. Researchers have found that diabetes is associated with reduced bacterial populations and reduced microbial diversity in saliva [30, 31]. According to a study [32], individuals with poor glucose tolerance had higher levels of salivary microorganisms like Catonella, Leptotrichia, Staphylococcus, and Bulledia. Researchers found in a 2019 study by Wang et al. that individuals with poorly controlled glucose clearance tended to have higher levels of Bulledia, Leptotrichia, Catonella, and Staphylococcus in their saliva [33]. A study of Kuwaiti children also found that DNA-DNA interbreeding decreased bacterial diversity and increased salivary glucose levels [34]. It was also found that patients with gestational diabetes had higher levels of P. gingivalis and T. forsythia. However, PCR-based studies found that obese patients had higher levels of P. gingivalis and T. forsythia in their salivary samples regardless of their type 2 diabetes [35]. As a result, these findings suggest that type 2 diabetes and poorly controlled glucose clearance may have similar effects on salivary microbiota.

Oral Cancer

The role played by salivary microbiota in the spread of cancer may benefit the development of new therapies and early cancer diagnosis. The microbial makeup of saliva from cancer patients differs from that of healthy people, and it has long been recognized that the microbiome can be used as a diagnostic indicator. A study by Mittal et al. [36] found that high levels of Streptococcus mutants and lactobacilli in saliva are associated with oral disease prevalence, while another study associated high levels of Capnocytophaga gingivalis, Prevotella melaninogenica, and Streptococcus mitis in saliva with oral cancer risk.

Using next-generation sequencing, researchers evaluated the salivary microbiome of people with oral squamous cell cancer (OSCC). The most important finding was that people with OSCC had higher levels of periodontitis-related species in their saliva, including Prevotella tannerae, Fusobacterium nucleatum, and Porphyromonas intermedia. The salivary microbiome of patients with leukoplakia, epithelial precursor lesions, and OSCC has been documented [37].

Bacillus, Enterococcus, Parvimonas, Peptostreptococcus, and Slackia are distinct bacteria found in patients with OSCC. Mager et al. [38] examined the microbial diversity in case–control studies and found that the species Capnocytophaga, Prevotella, and Streptococcus can be used to diagnose OSCC.

A pilot study of 11 patients with OSCC and squamous cell carcinoma (SCC) in the oral cavity found 17 bacterial phyla in their saliva [39]. In all operational taxonomic units (OTUs), Firmicutes, Bacteroidetes, Actinobacteria, and Proteobacteria were more significant than other bacterial phyla. There were a number of marginal species found in the saliva of the patient [40–42] including Fusobacteria, Spirochaetes, TM7 (Saccharibacteria), Tenericutes, Cyanobacteria, Synergistetes, SR1 (Absconditabacteria), Thermi, GN02 (Gracilibacteria), Chloroflexi, Armatimonadetes, OP3 (Omnitrophica), and Verrucomicrobia.

A study conducted in 2021 by Qian et al. [43] examined the unstimulated saliva of patients with salivary adenoid cystic carcinomas (SACCs) and healthy controls. While beta diversity was found to have a separation tendency, alpha diversity did not differ significantly between SACC patients and healthy control volunteers. Moreover, healthy controls had higher concentrations of Prevotella and Alloprevotella than SACC patients, but SACC patients had higher concentrations of Streptococcus and Rothia.

Cancer treatment changes the salivary bacterial niche by radiation. Among other things, radiation exposure altered the flow rate and pH of saliva, as well as the number of Lactobacillus species present. The bacteria Streptococcus, Actinomyces, and Veillonella were found to be lower in oral mucositis caused by chemotherapy, while the periodontitis-related genera Fusobacterium and Prevotella were found to be more abundant [44].

Non-oral Cancer

Other malignancies, besides oral tumors, have been found to exhibit specific alterations in salivary microorganisms. The leading cause of mortality worldwide is lung cancer. Even though smoking is often associated with lung cancer, figures from around the world indicate that smoking does not account for 53 percent of lung cancer in women. Consequently, it was assumed that women's lung cancer incidence was influenced by hormones that are gender-dependent. Researchers identified salivary microbiomes as highly related to lung cancer in nonsmoking women in Yang et al. [45]. The study found that the microbial diversity and richness of salivary microbiota from non-smoking female lung cancer patients were lower, with a notable rise in Blastomonas and Sphingomonas in lung cancer patients. During the same period, levels of Streptococcus and Acinetobacter were higher in controls. Moreover, lung cancer patients had higher levels of Veillonella and Streptococcus species in their saliva. A number of recent investigations have also found streptococcus in the lungs of patients with cystic fibrosis and chronic obstructive pulmonary disease (COPD). With a novel electrophoresis technique based on recycled isoelectric focusing, Yuan et al. [46] investigated the association between salivary microbiota and lung cancer.

According to Farrel et al. [47], salivary microbiomes might serve as biomarkers for identifying pancreatic cancer at an early stage, before the disease progresses. In a recent study, Farrell et al. found that patients with pancreatic cancer had significantly higher ratios of Leptotrichiato and Porphyromonas in their saliva than patients with healthy or other diseases, and lower levels of Neisseria and Aggregatibacter. According to this study [48], these ratios could be used as biomarkers for early-stage pancreatic cancer. By using the Human Oral Microbe Identification Microarray (HOMIM), researchers discovered that patients with pancreatic cancer had lower levels of Neisseria elongata and Streptococcus mitis than healthy individuals.

There was a significant association between precancerous stomach lesions and T. forsythia and A. actinomycetemcomitans in people with cancer outside the oral cavity [49]. A review of case–control studies showed a link between salivary microbiome and digestive tract cancer (DTC) in these cases. The bacteria in DTC patients were more diverse than those in control patients. Researchers found that DTC patients have significantly higher levels of Porphyromonas gingivalis in their saliva. Further, it was found that F. nucleatum, a periodontal pathogenic OUT, was significantly more prevalent among individuals with tongue/pharyngeal and esophageal cancers [50].

Identifying the link between salivary microbiota and non-oral cancer is crucial to clarifying the etiology of cancer. For cancer diagnosis or prediction, a concept based on bacterial makeup can be useful. By observing the salivary microbiota, we may be able to predict cancer and preserve our health. We may be able to better understand illnesses in the future thanks to these insights, and we may also be able to develop new approaches to diagnosing and treating diseases.

Irritable Bowel Syndrome (IBS)/Irritable Bowel Disease (IBD)

Symptoms of irritable bowel syndrome (IBS) can vary from patient to patient and can also appear in people with other illnesses, making diagnosis difficult. IBS costs more than $20 billion a year to diagnose. Because of their heterogeneity, IBS symptoms are challenging to define, and many medical practitioners believe that the "catch-all" diagnosis of IBS requires further testing in order to exclude more serious illnesses. In primary care, however, the limited use of symptom-based diagnostic criteria may hinder accurate diagnosis of IBS because it can lead to unnecessary, extensive diagnostic testing and therapeutic delays.

IBS can be diagnosed with the salivary microbiome without the need for invasive procedures, increasing accuracy and reducing errors in the diagnosis process. As a result of the salivary microbiota, unnecessary and expensive diagnostic tests will be less necessary, resulting in shorter treatment delays. Chong et al. [51] report that salivary microorganisms in IBS patients differ from those in other salivary samples. According to the study, salivary samples from individuals with IBS had almost the same diversity and richness of microbial species as gut samples. Moreover, IBS patients' saliva showed a significant decrease in Streptococcus and an increase in Prevotella, a gram-negative, anaerobic bacteria. Qi et al. [52] found that a decreased number of Streptococcus species and an increased number of Prevotella species can cause opportunistic infections, such as esophagitis, bacterial vaginosis, salivary caries, and antral gastritis, which are indicative of atypical physiologies in IBS patients.

Rheumatoid Arthritis

There are about five out of every thousand people who suffer from rheumatoid arthritis, a condition that causes severe joint damage, edema, and inflammation. Women are more likely to suffer from RA than men, and the disease is more common in older people. In contrast, early diagnosis and treatment of RA can prevent joint erosion and slow the progression of erosive arthritis, which can improve the course of the disease. Whether an illness enters remission depends on a timely diagnosis and treatment course. It is difficult to distinguish early RA from other conditions when symptoms first appear, and the American College of Rheumatology's suggested criteria cannot identify early RA because there is little clinical and laboratory evidence.

A link exists between RA development and oral microbial dysbiosis. RA development and oral microbial dysbiosis can be slowed by early detection by the salivary microbiota. In accordance with epidemiological and translational studies, environmental exposure and dysbiosis in mucosal locations (lung, intestinal tract, and oral cavity) may play a crucial role in the onset of RA. In response to citrullinated antigens, Porphyromonas gingivalis may produce the PAD enzyme [53], and antibodies against this pathogen were found by Lappin in 2013 [54] to be positively correlated with an increase in anti-citrullinated protein antibodies (ACPA). A new species of citrullinated antigens was also identified by Konig in 2016 [55] in the oral regions of RA patients. As Hirschfeld [56] points out, Aggregatibacter actinomycetemcomitans stimulates neutrophil migration and growth in extracellular traps, which leads to PAD dysfunction and the release of highly citrullinated proteins. Moreover, two periodontal infections, P. gingivalis and Prevotella agigrescens, aggravate collagen-induced arthritis in rats. According to these results, oral dysbiosis produces autoantigens and is associated with RA.

A dramatic change in oral microbiome has been observed in RA patients [57, 58], but little is known about their status in the early stages of the disease. This alteration may occur before clinically apparent arthritis and may be related to the disease development or may just be a symptom. In people with ACPA, arthralgia is more likely to develop RA, as ACPA is highly specific, detectable early, and predicts rapid deterioration of the disease [59] (Table 2).

Table 2.

A summary of data from research that has linked the salivary microbiome with non-oral diseases

Diseases	Disease-related characteristics of the salivary microbial community		Reference
	Increased microbial levels	Decreased microbial levels
Rheumatoid arthritis	Lactobacillus salivarius	Haemophilus spp.	Zhang et al. [60]
Celiac disease	Bacilli, Selenomonas, Actinomyces oris	Lachnospiraceae, Gemellaceae, Streptococcus sanguinis, Porphyromonas sp., Prevotellananceiensis, Rothia, SR1	De Angelis et al. [61]
Inflammatory bowel disease	Lachnospiraceae, Gemellaceae, Streptococcus sanguinis, Porphyromonas sp., Prevotellananceiensis, Rothia, SR1	Bacilli, Selenomonas, Actinomyces oris, decreased alpha diversity	Said et al. [62]
Nasopharyngeal carcinoma	Firmicutes Neisseria Leptotrichia Pseudomonas	Proteobacteria Streptococcus	Xu et al. [63]
Pancreatic cancer	Porphyromonasgingivalis Aggregatibacter Actinomycetemcomitans	Fusobacteria Leptotrichia	Fan et al. [64]
HIV-AIDS	Porphyromonas, Treponema, Eubacterium		Lewy et al. [65]
Atherosclerosis	Anaeroglobusin	Parvimonas, Capnocytophaga, Catonella, Lactobacillus	Kageyama et al. [66]
Hepatic encephalopathy	Prevotellaceae, Fusobacteriaceae, Enterococcaceae	Enterobacteriaceae	Bajaj et al. [67]
Antibiotic treated Low birth weight neonate	Pseudomonas aeruginosa, Ureaplasma parvum	Mycoplasma spp.	Costello et al. [68]

Case Studies Illustrating Practical Applications of Salivary Microbiota as a Diagnostic Tool

Various reports available on the case studies explaining the potential of applications of use of salivary microbiota for diagnosis of various diseases associated with human. A study by Wang et al. [69] investigated the saliva of 10 metabolic-associated fatty liver disease (MAFLD) patient and 10 healthy individuals. The authors have concluded that based on the redundancy analysis and spearman correlation analysis the clinical variables related to insulin resistance and obesity are strongly associated with the microbial community and also suggested that patients with MAFLD displayed alterations in the salivary microbiome ecosystem, and the saliva microbiome-based diagnostic model represents a promising avenue for supporting MAFLD diagnosis. Similarly, in yet another study Qing et al. [70] reported the role of the oral microbiome in schizophrenia by analyzing the salivary microbiome of 85 patients with drug-naïve first-episode schizophrenia (FES), 43 individuals at clinical high risk (CHR) for psychosis, and 80 healthy controls (HCs) using 16S rRNA gene sequencing. The salivary microbiome of FES patients was characterized by higher alpha diversity and lower beta diversity heterogeneity than those of CHR subjects and HCs. Interestingly, the study established a link between salivary microbiome alterations and disease initiation and provided the hypothesis of how the oral microbiota could influence schizophrenia. In line with this the study by Cesic et al. [71], reported association between salivary microbiota and chronic spontaneous urticaria (CSU), where 13 patients diagnosed with CSU and 10 healthy controls were involved in the study. Salivary microbiota was analyzed using a molecular approach that targeted 16S ribosomal RNA, and terminal restriction fragment length polymorphism (T-RFLP) and found that the alpha diversity of salivary microbiota in CSU patients was significantly reduced compared to healthy controls, resulting in a change in community composition. The species richness, determined using the Shannon index, was significantly reduced in the CSU group.

There are many more case studies are available which can provide the knowledge of salivary microbiota of various human diseases and the comparative analysis of patient and healthy individuals of multiple diseases. Those study includes Gastroesophageal reflux disease (GERD) by Qian et al. [72], atherosclerotic cardiovascular disease (ACVD) by Kato-Kogoe et al. [73], obstructive sleep apnea comorbid hypertension by Chen et al. [74], primary biliary cholangitis (PBC) by Lv et al. [75], frailty by Ogawa et al. [76], immunoglobulin A nephropathy by Khasnobish et al. [77], digestive tract cancer by Kageyama et al. [78], recurrent aphthous stomatitis (RAS) by Kim et al. [79].

Along with the mentioned case studies, Table 3 summarizes the different salivary biomarkers particular in content to the infectious disease. Increase in a particular salivary microbiota can be useful as a biomarker for the early identification of the particular disease.

Table 3.

List of different salivary biomarkers in context with the infectious diseases

Sr. No	Disease	Salivary biomarkers	Inference	References
1	Covid19	Neisseria and Bacillus	The relative abundant by 1.6-fold for Neisseria in COVID-19-positive patients found as compared to COVID-19 negative person.	[80]
2	Asymptomatic COVID-19	Lautropia mirabilis	Three major species in salivary bacteriome including Veillonella parvula, Streptococcus mitis, and Prevotella melaninogenica were observed in in the asymptomatic COVID-19 positive group and negative group.	[81]
3	SARS-CoV-2 infected asthma and non-asthma cases	Actinobacteriota and Pseudomonadota	Actinobacteriota and Pseudomonadota are enriched in the SARS-CoV-2-non-asthma group and SARS-CoV-2 asthma group of the salivary microbiome, respectively.	[82]
4	Human papillomaviruses	Oral squamous cell carcinoma	Oral transmission of human papillomavirus from infected spouse to partner spouse has tenfold higher risk.	[83]
5	Legionnaire’s disease	Legionella pneumophila	As the natural reservoir for L. pneumophila and other Legionella species is aquatic habitats. Detection of Legionella pneumophila in saliva is convenient method for detection of Legionnaire’s disease.	[84]
6	Tuberculosis	Mycobacterium tuberculosis	The higher microbial load of Mycobacterium tuberculosis in compared to healthy individual can be an indicator of use of saliva as a biomarker in diagnosis of tuberculosis.	[85]
7	Gonorrhea and Syphilis	Neisseria gonorrhoeae (which causes) and Treponema pallidum (which causes syphilis)	Infants have contracted syphilis by the mouth‐to‐mouth transfer of prechewed food from actively infected relatives.	[86]
8	HIV‐infected individuals	Yeasts	Candida albicans transmission between spouses can take place through saliva.	[87]
9	Infectious mononucleosis (kissing disease)	Epstein–Barr virus‐induced mononucleosis	The Epstein–Barr virus is transmitted through direct contact with virus‐infected saliva and rarely via. air and blood.	[88]

Various Indian research organization are working to explore the potential of the salivary microbiota and Table 4 summarizes the findings of the Indian research institute.

Table 4.

List of salivary biomarkers identified by the Indian Institutes

Sr. No	Indian Institute	Subjects for the study	Important highlights of the study	References
1	Defence Research and Development Organization (DRDO) and Defence Institute of Physiology and Allied Sciences (DIPAS), New Delhi, India.	Twelve volunteers of expedition team who stayed in the Indian Antarctic Station, Maitri (70° 45′E, and 11°44′S), which located in the Central Dronning Maudland region of east Antarctica, about 100 km inland from the Princess Astrid coast.	The compositional and functional differences in the oral microbiota of Antarctic expedition members was observed after 25 days (T2) and after 30 days (T3) At T2, significant ↑Proteobacteria, ↑ Bacteriodetes and ↑Fusobacteria and ↓Firmicutes At T3, ↑ Enterococcus, ↑Pseudomonas, ↑Neisseria, ↑Fusobacterium, ↑Haemophilus, ↑ Veillonella and ↓Streptococcus and ↓Lactobacillus.	[89]
2	Laboratory of Genomics and Profiling Applications, Centre for DNA Fingerprinting and Diagnostics (CDFD), Hyderabad, Telangana. India.	Eight different locations throughout India was identified and analysis of salivary samples was done from 92 volunteers by amplifying and sequencing variable regions (V1 and V2) of the bacterial 16S rRNA gene.	Solobacterium spp., Lachnoanaerobaculum spp. and Alloprevotella spp. were observed to be a component of the saliva microbiome unique to Indian populations.	[90]
3	Dr. D. Y. Patil Dental College and Hospital, Pune, India and M. A Rangoonwalla Dental College and Hospital, Pune, India.	Fifty-one adult volunteers who had been previously treated for any active periodontal disease or caries and were recruited for the study the relationship between salivary microbiome and subclinical inflammation to at the Dr. D. Y. Patil Dental College and Hospital, Pune, India.	The genera Abiotrophia, Anaerobacillus, Micrococcus, Aggregatibacter, Halomonas, Propionivivrio, Paracoccus, Mannhemia, unclassified Bradyrhizobiaceae, and Caulobacteraceae were significant indicators of high IL- 1β	[91]
4	Dr. B. Borooah Cancer Institute, A. K. Azad Road, Gopinath Nagar, Guwahati, Assam, 16, India. And Department of Life Sciences, School of Life Sciences, Central University of Tamil Nadu, Thiruvarur, 610101, India.	Twenty-five patients with OSCC and 24 healthy controls were recruited from Dr. B. Borooah Cancer Institute (BBCI), Guwahati, Assam, India.	↑ P. melaninogenica, Fusobacterium sp., ↑P. endodontalis, ↑V. parvula, ↑P. pallens, ↑Dialister, ↑S. anginosus, ↑P. nigrescens, ↑C. ureolyticus, ↑P. nanceiensis, ↑P. anaerobius, and increase in other non microbial inflammation biomarkers in the saliva of OSCC cancer patients may be considered as a liquid biopsy-based biomarker for OSCC cancer patients in our study.	[37]
5	Pathophysiology and Disruptive Technologies, Defence Institute of Physiology and Allied Sciences (DIPAS), Defence Research and Development Organisation (DRDO), Lucknow Road, Timarpur, Delhi, 110054, India.	Study the effect of high altitude on oral microbiome was done on total 16 subjects at two different hights from the sea level H1 (210 m) and H2 (4420 m).	Different altitudes suggest variations in the oral microbiome. Abundance of Firmicutes, Bacteriodetes, and Actinobacteria was decreased at and predominance of Proteobacteria was observed H2 compared to H1.	[92]
6	CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), New Delhi, India. Academy of Scientific & Innovative Research (AcSIR), CSIR-IGIB, New Delhi, India Department of Clinical Immunology and Rheumatology, Christian Medical College Hospital, Vellore, India.	Analysis of salivary samples of 37 patients with primary Sjögren's syndrome and 35 healthy controls was done.	↑Bifidobacterium, Dialister and Lactobacillus and ↓Leptotrichia was observed in primary Sjögren's syndrome compared to the controls.	[93]

Challenges of Imaging the Salivary Microbiome

Imaging the salivary microbiota of a patient, saliva samples can be collected 30 min before and an hour after eating. Participants are given a DNA stabilizer to drink, then asked to spit into a collecting tube. The DNA is extracted by bead beating, and its amount is measured using Qubti. Using a polymerase chain reaction, high-quality genomes are sequenced, and 97 percent of the genomes are mapped to a database. The numerical modeling criteria are based on the intended number of recognized genomes per sample. In order to evaluate length and unclear bases, Illumina MiSeq sequences are used to amplify and sequence the 16S ribosomal subunit V3-V4. Bioinformatics investigation is used to conduct statistical analyses to identify significant characteristics based on their abundance. Microbiome analyst can be used to perform a univariate analysis, which looks for statistically significant variations in the abundance of taxa. Figure 3 illustrates the detailed process for salivary analysis. Several organizations have adopted next-generation sequencing, including the Human Microbiome Project, which has developed the largest database on the human microbiome to date.

Fig. 3.

Stepwise procedure for detection of salivary microbiota

A large database of salivary microbiomes can be used to investigate non-oral disorders using this non-invasive method. However, patterns of salivary microbiome that are circadian-dependent may affect detection. Next-generation sequencing, according to some researchers, may reveal biases in PCR amplification and hide the influence of bacterial abundance over bacterial diversity, resulting in inaccurate microbiome analyses. Moreover, it is difficult to measure low-abundance bacteria, reducing metaproteomics' potential. To gain a deeper understanding of the process, it is necessary to improve methods for microbe identification and analysis, and to support downstream processes. Microbiota should have been a useful biomarker for diagnosis, but the small sample size led to different conclusions. When examining saliva samples from the same individual over time, one can identify the differences between various illness phases and individual differences within the same individual. Gene composition has a substantial amount of variation when compared to the divergence across human populations, and a similar percentage of that diversity may be attributed to genetic variation within genes themselves. In order to determine the most effective method of conducting future research, it will be crucial to improve the metadata collection of information such as diet and oral health (i.e., periodontal disease) as well as to sample saliva from the same individual over time to assess whether it is possible to distinguish between disease stages and control for intra-individual variation.

Future Research Directions and Associated Challenges

Multiple aspects are considered for understanding the future research directions and various challenges for developing the promising salivary biomarkers for diagnosis of various diseases.

Exploration of Microbial Signatures: Future research should focus on identifying specific microbial signatures within saliva that can serve as reliable biomarkers for various diseases. This entails a comprehensive analysis of the microbial composition to pinpoint exact species or strains associated with different health conditions. Challenges include the need for large-scale, longitudinal studies and advanced sequencing techniques to capture this nuanced data [94].

Standardization of Sampling and Analysis: Developing standardized protocols for saliva sampling and microbial analysis is crucial to ensure the reproducibility and comparability of research findings. Challenges lie in creating universally accepted guidelines that accommodate variations in individual microbiomes and accounting for environmental factors [95].

Integration of Omics Technologies: Combining metagenomics, metatranscriptomics, and metabolomics approaches can provide a more holistic understanding of the oral microbiota and its functional roles in health and disease. Challenges include data integration, bioinformatics complexity, and the need for multi-disciplinary collaborations [96].

Validation Studies: Extensive validation studies are essential to establish the accuracy, sensitivity, and specificity of saliva-based diagnostic tools. Large-scale clinical trials and validation cohorts are needed to confirm the diagnostic potential of identified biomarkers. Challenges include resource-intensive efforts and long-term data collection [97].

Longitudinal Monitoring: Research should investigate the utility of saliva microbiota for long-term disease monitoring, tracking disease progression, and assessing treatment efficacy. Challenges include designing studies with extended follow-up periods and addressing ethical considerations related to privacy and data security [98].

Ethical and Regulatory Considerations:

As saliva-based diagnostics become more prominent, ethical and regulatory frameworks need to be developed or updated to ensure patient privacy, informed consent, and responsible data handling. Compliance with these standards is essential to maintain public trust in these non-invasive diagnostic tools [99].

Translation to Clinical Practice: Bridging the gap between research findings and clinical implementation is a significant challenge. Future research should explore strategies for integrating saliva microbiota analysis into routine healthcare settings, including training healthcare professionals and developing user-friendly diagnostic tools [100].

Disease-Specific Applications: Investigating disease-specific applications of saliva microbiota diagnostics is an exciting avenue for future research. Tailoring diagnostic approaches to specific conditions, such as cancer, autoimmune diseases, or infectious diseases, may yield more precise and actionable results [101].

Global Diversity and Cultural Factors: Recognizing the global diversity of oral microbiomes and considering cultural factors that may influence microbial composition is crucial. Future research should address these variations to ensure the applicability of saliva-based diagnostics across diverse populations [102].

In summary, future research in the field of saliva microbiota as a non-invasive diagnostic tool holds great promise but also faces several challenges. Overcoming these challenges will be essential to harness the full potential of this emerging approach and translate it into clinical practice, ultimately improving early disease detection and healthcare outcomes.

Cost-Effectiveness of Saliva as a Diagnostic Tool

Compared to the current diagnostic tools the saliva as a diagnostic tool has numerous cost-effective benefits. Firstly, the use of saliva for this purpose is very affordable along with its simplicity to use and also it is non-intrusive. It is also very simple considering its administration as compared to serum which requires needle people generally scared for use of needles because of pain cause and it also requires the professional and qualified medical personnel for which also the bearing the cost will matter. Sample storage and transport also requires the low cost compared to serum samples. There are many screening assays are available with saliva samples and it requires minimum manipulations during diagnostic procedure in contrast to serum.

Conclusion

Salivary microbiota analysis has emerged as a highly promising diagnostic tool for both oral and non-oral health conditions. Over the past decade, numerous studies have demonstrated that analyzing the microbial communities present in saliva can provide valuable insights into an individual's health status. This non-invasive approach offers a straightforward, cost-effective, and simple means of identifying various diseases and disorders.

The human microbiome consists of a diverse range of microorganisms that play crucial roles in our biological systems and are implicated in numerous diseases. Traditional culture-independent methods may underestimate the significance of low-abundance bacteria, necessitating the development of more effective techniques for their enrichment and comprehensive analysis. Fortunately, the salivary microbiome, which varies from person to person, remains relatively stable over long periods, even spanning years.

An increasing body of evidence suggests that the salivary microbiota is not only relevant to oral health but also holds connections to non-oral illnesses. This exciting prospect, coupled with the ease of collecting saliva samples, indicates that salivary microbiota research could have substantial diagnostic and prognostic applications. While several studies support the idea that the bacterial composition in saliva can serve as a biomarker for diagnosing illnesses, further in-depth investigations involving patients are necessary to confirm their prognostic value.

Although the results thus far are promising, it is important to conduct additional research to establish standardized protocols for salivary microbiota analysis and to validate its clinical utility. Nonetheless, salivary microbiota analysis has immense potential as a versatile, non-invasive strategy for diagnostic applications in various medical fields.

Funding

No external funding received.

Declarations

Conflict of interest

The authors declare no conflicts of interest with respect to authorship for publication of this review article.

Consent to Participate

All authors have consented and have no objection for publication of the article.