For generations, one might say, we carpet-bombed the mouth with antiseptic mouthwash, marketed sterility as hygiene, and constructed entire health systems around the assumption that the oral cavity was a plumbing problem, disconnected from the rest of the body. The science is now telling a story so different that it borders on the embarrassing. These microbial communities regulate acidity, protect enamel, and perform a function that is only recently being understood at the mechanistic level: they convert dietary nitrate into nitric oxide, the molecule that controls blood vessel tone and blood pressure. A 2025 study from the University of Exeter, published in Free Radical Biology and Medicine, demonstrated this directly. When older adults drank nitrate-rich beetroot juice twice daily for two weeks, their oral microbiome shifted: harmful Prevotella declined, beneficial Neisseria increased, and blood pressure fell. The mechanism ran through the mouth. Destroy those bacterial communities with aggressive mouthwash and one interrupts the nitrate-nitric oxide pathway. Blood pressure rises. Periodontal dysbiosis has also been associated, in observational studies, with cardiovascular events and diabetes complications. A growing body of research is investigating possible links to neurodegenerative conditions, though causal pathways remain to be established. The mouth was never a silo. It was a sentinel. We treated it like the extremity of a tube.

And within the mouth, there is a surface that strikes me as the most overlooked diagnostic site in the human body: the tongue. Its dorsum hosts dense, structured bacterial communities organised in complex biofilms, and these communities are not passive. They are a primary site for the nitrate-nitrite-nitric oxide pathway that the Exeter study exploited. A 2026 study published in npj Biofilms and Microbiomes classified tongue microbiota from 729 individuals into three distinct orotypes, each associated with different metabolic and oral health outcomes, with temporal stability observed over six years. Separately, research has linked tongue microbiome alterations to conditions ranging from rheumatoid arthritis to gastrointestinal cancers to pneumonia in the elderly. The tongue, in other words, is a readable, persistent, individually variable biological surface that may one day function as a non-invasive diagnostic interface. And we are still telling patients to scrape it and move on.

This is changing, and faster than most clinicians appear to realise. Saliva carries over three thousand identified proteins, microbial signatures, metabolites, and immune markers. It is, if one thinks about it clearly, a liquid biopsy produced continuously without a needle. Researchers are training AI to read it: models that detect early signals of diabetes, cardiovascular disease, and kidney failure from salivary patterns alone. A team in China built an AI periodontitis screening tool with ninety-four percent accuracy on panoramic X-rays, designed not for private clinics but for underserved community health centres. The oral microbiome is becoming readable, actionable, predictive. And almost nobody in mainstream healthcare is paying attention.

At-home oral microbiome test kits already exist. One can order them today. A growing number of startups ship saliva collection devices to your door, sequence your oral bacteria, and return reports identifying the specific pathogenic species driving your cavities, your gum inflammation, your chronic bad breath. They map the ratio of beneficial to harmful organisms. They recommend targeted probiotics and dietary changes based on your actual microbial profile. Your dentist, in all likelihood, has never heard of any of them.

One must be fair. This is early. Different labs use different sequencing methods. The same saliva sample can produce meaningfully different results depending on who analyses it. Reference databases are incomplete. Standardisation is thin. The clinical evidence linking specific microbial profiles to specific interventions is growing but far from settled. This is a first generation, not a finished product. But the trajectory, I think, is obvious. As AI learns to read microbial patterns at scale, as sequencing costs continue to fall, as the science of the oral ecosystem catches up with the technology built to measure it, the precision will follow. It always does.

And the oral microbiome is not arriving alone. Biosensor patches are being developed that sit on the gum and track pH, inflammatory markers, and microbial shifts in real time. Smart toothbrushes embedded with high-definition oral scanners are entering the market, with imaging analysed by AI and reviewed by remote professionals. Hydroxyapatite toothpaste, which remineralises enamel without destroying the microbiome, has been standard in Japan since 1993, born from a NASA patent for astronaut bone loss, with over 160 million tubes sold across Asia and still virtually unknown in the West. In Japan, Dr Katsu Takahashi has begun human trials for a tooth regrowth drug, a peptide that reactivates dormant stem cells in the jawbone. At King’s College London, researchers grew early tooth-like structures in a laboratory in 2025. The liquid biopsy, the smart brush, the microbiome-compatible chemistry, the regenerative biology: these are not isolated innovations. They are converging.

Which brings us to the question that, to my mind, actually matters. What happens to the dentist? Not in the abstract. Concretely. What does the profession look like in 2035 when the mouth has become a data stream, when patients arrive already knowing their microbial profile, when AI has pre-screened their imaging before the appointment begins?

I see three scenarios. 

The first is inertia. One does nothing. Dental systems continue on a model designed decades ago: episodic, reactive, centred on repair. The microbiome stays a curiosity. The tools stay consumer gadgets. Western dental workforces keep shrinking under chronic shortages while chronic oral dysbiosis silently accelerates cardiovascular disease and cognitive decline in ageing populations. Nobody connects the dots because the microbiome was never woven into the care pathway. This is the default. It requires no decision. That is precisely what makes it dangerous.

The second is augmentation. Data-driven tools are layered onto the existing model. The dentist remains central but evolves into a clinician who interprets biological data, manages microbial ecosystems, and coordinates with primary care. AI pre-analyses scans. Microbiome reads inform treatment plans. Saliva-based screening flags systemic risk alongside periodontal risk. The chair stays, but what happens in it changes fundamentally. This is the pragmatic path and probably the necessary first step.

The third scenario is harder to picture but worth taking seriously. In this version, the bathroom becomes the first diagnostic room in the house. Continuous monitoring replaces periodic visits. Toothpaste is prescribed by algorithm, matched to the current state of the ecosystem. Quarterly saliva samples, analysed by AI, screen for systemic risk months before conventional symptoms appear. The dental surgery as a standalone institution dissolves into a broader oral health node integrated into primary care. The clinician still exists, but the centre of gravity shifts from the chair to the patient’s daily routine, from repair to cultivation, from the clinic to the home. Whether this is realistic in five years, fifteen, or fifty is genuinely open. But to dismiss it entirely is to ignore where every converging technology is pointing.

Whichever scenario unfolds, one principle holds. The oral microbiome is not a dental concern. It is a medical frontier. And it demands action now, not when the science is perfect, but while the systems that will deliver it can still be shaped. One must integrate microbiome screening into primary care. Subsidise microbiome-aware tools for the populations locked out of private care: saliva tests, smart brushes, AI triage deployed in pharmacies and schools. Shift the cultural narrative from sterilisation to cultivation. A healthy mouth is not a germ-free mouth. It is a balanced one. And redesign dental training now, because whichever future arrives, the clinicians who inhabit it will need fluency in microbiology, data science, and regenerative medicine, not just restorative technique.

Seven hundred species are broadcasting. They carry data about your heart, your metabolism, your brain. The tools to listen are arriving. The profession that should be listening the hardest is, so far, the quietest in the room. If dentistry does not claim this frontier, medicine will. And dentistry will have no one to blame but itself.

Et voilà.

The findings, published in Cell Host & Microbe, help improve microbial identification, highlight oral-gut microbial connections, and provide a resource for studying oral health. 

Modern DNA-based methods have revealed many previously unknown species, but existing databases mostly cover bacteria that are easily cultured in the lab. So, researchers led by Jun Hyung Cha at Yonsei University in Seoul, Korea, set out to assemble high-quality microbial genomes from thousands of oral DNA samples.

Comprehensive catalog

The researchers created a microbial catalog, which they called HROM (human reference oral microbiome), containing 72,641 high-quality genomes from 3,426 species. More than half of these species had not been described before. By providing a much larger reference than existing catalogs, HROM allows scientists to identify oral bacteria more accurately in DNA samples, the authors say.

The team also identified more than 1,100 new species of Patescibacteria, which have reduced genomes and live as parasites on other bacteria in the mouth. Despite their small size, Patescibacteria appear to play an important role by contributing to biofilm formation, protecting other bacteria from stress, delivering nutrients, and defending against viruses.

A previously unknown group of these bacteria was associated with periodontitis, a severe form of gum disease. Together with the well-known pathogen Porphyromonas gingivalis, these bacteria help predict periodontitis, the researchers found.

Disease links

Comparing HROM to a comprehensive catalog of the gut microbiota, the team identified 330 bacterial species present in both environments. Some species live in both the mouth and the gut, while others are typically found in the mouth but occasionally appear in the gut.

These bacteria are enriched in people with heart, gut, and liver conditions, suggesting that they may contribute to systemic inflammation. For example, several of these species could predict colorectal cancer, the authors say.

The findings suggest that oral microbes may influence health far beyond the mouth, they add. “HROM offers an expanded view of the oral microbiome and highlights the clinical importance of further examining the links between oral microbes and systemic disorders.”

The findings, published in Cell Host & Microbe, suggest that tracking the mouth microbiota can help identify ECC risk early and inform prevention strategies in children. The tool, the authors say, “can potentially serve as an objective, sensitive, and patient-friendly measure of ECC susceptibility.”

Scientists have long been trying to use the mouth microbiota to predict ECC, but current methods are often unreliable, especially for predicting new cases in young children. This is because they don’t fully consider how oral bacteria change over time or differ between areas.

Over two years, researchers led by Fei Teng at Sun Yat-sen University in Guangzhou, China, tracked changes in oral bacteria from saliva and dental plaque of 50 children to understand how these microbial communities develop and respond to tooth decay.

Microbiota development

The children were divided into three groups: those who stayed healthy, those who developed cavities during the study, and those whose cavities worsened. The researchers found that as healthy children aged, their oral microbiota matured in a predictable way, especially in plaque. 

However, children who developed ECC had a disrupted or delayed microbial development. The findings, the authors say, suggest that tooth decay interferes with the normal development of oral bacteria. 

Using machine learning, the team identified specific bacteria that indicate a child’s “microbiota age,” and found that children with ECC often had younger or less mature microbial profiles than their healthy peers.

Spotting decay

The researchers also found that the biggest shift in oral bacteria happens when caries first appear. After this initial change, the bacterial community tends to stabilize, even if the cavities get more severe. 

Using these bacterial changes, the team built a predictive model that can identify children at risk of developing caries before symptoms appear, mainly by looking at specific groups of bacteria, such as Prevotella, in both plaque and saliva. The model showed good accuracy in predicting which kids would develop ECC, the researchers found. 

The results show that “caries onset in apparently healthy teeth can be predicted using microbiota,” the authors say. The findings, they add, not only offer a new way to spot tooth decay early but also highlight how tracking mouth bacteria over time can improve understanding and prevention of oral diseases.

What this research adds
Researchers investigated the metabolism of S. mutans in the presence of sucrose — the main constituent of white sugar. Sucrose generally boosted lysine lactylation, especially in proteins involved in metabolism and protein synthesis. An enzyme called GNAT13 catalyzed lysine lactylation onto a subunit of the protein involved in the synthesis of RNA molecules from a template of DNA. Overexpressing GNAT13 in S. mutans inhibited the formation of clusters of bacteria that are known as biofilms.

Conclusions
The findings suggest that lysine lactylation contributes to metabolic regulation in bacteria and that GNAT13 may limit sucrose-driven biofilm formation.

Snacking on sweets can lead to tooth decay, as mouth bacteria feed on sugars and then process them into acids that slowly cause cavities. Now, scientists have found that sugar stimulates a type of protein modification that shifts the metabolism of cavity-causing bacteria.

The study, published in Science Signaling, also identified a potential target to limit the formation of dental plaque — the sticky film that coats teeth and where cavity-causing bacteria typically reside.

The most common of these bacteria is Streptococcus mutans, which relies on dietary sucrose for energy. As S. mutans metabolizes sucrose, it produces lactate, or lactic acid, which damages enamel, leading to cavities. S. mutans also incorporates lactate into the carbohydrate chains that bind bacteria together into clusters known as biofilms. 

Lactate can undergo different chemical modifications, including one, called lysine lactylation, where enzymes called acetyltransferases attach the lactyl group of lactate to certain lysine amino acid residues in proteins. 

Scientists have known that in mammalian cells, lysine lactylation links cellular metabolism to changes in the activities of proteins that control gene expression. However, it is unclear whether lysine lactylation plays a role in how S. mutans metabolizes dietary sugars.

To address this question, researchers led by Zhengyi Li at Sichuan University in Chengdu, China, investigated the metabolism of S. mutans in the presence of sucrose — the main constituent of white sugar.

Adapting to the environment

The researchers set out to identify and quantify the proteins undergoing lysine lactylation in S. mutans under various sucrose concentrations. Sucrose generally boosted lysine lactylation, especially in proteins involved in metabolism and protein synthesis, the researchers found.

Lysine lactylation induced significant changes in key enzymes involved in glycolysis — the process in which glucose is broken down to produce energy. The researchers also found that S. mutans can modulate essential biological processes, including gene expression, by increasing or decreasing lysine lactylation of various proteins in response to environmental conditions that drive the formation of biofilms.

“This ability plays a crucial role in enabling S. mutans to rapidly adjust its metabolic patterns, adapt to environmental changes, and facilitate biofilm formation,” the researchers say.

Metabolic regulation

Further experiments showed that an enzyme called GNAT13 catalyzed lysine lactylation and attached a lactyl group to a subunit of the protein involved in the synthesis of RNA molecules from a template of DNA. Overexpressing GNAT13 in S. mutans inhibited biofilm formation, the researchers found. 

“Although [lysine lactylation] has been demonstrated to regulate various metabolic processes in mammalian cells, the biological importance of this modification remains to be elucidated in prokaryotic cells,” the researchers say. “Our dataset provides a foundation for exploring the functions of [lysine lactylation] in bacteria.” 

The results suggest that lysine lactylation contributes to metabolic regulation in bacteria and may influence virulence and pathogenicity. The findings also indicate that GNAT13 may limit sucrose-driven biofilm formation, the authors say.

What this research adds
Researchers analyzed blood samples and joint fluids from people with rheumatoid arthritis with and without periodontal disease. People with periodontal disease experienced repeated flare-ups of rheumatoid arthritis and had higher levels of oral bacteria in blood, where the microbes were targeted by specific immune cells. These cells released anti-citrullinated protein antibodies (ACPAs), a type of antibodies against an individual’s own proteins that are commonly observed in people with rheumatoid arthritis.

Conclusions
The findings suggest that periodontal disease may contribute to rheumatoid arthritis by triggering specific immune responses.

Periodontal disease — the infection and inflammation of the gums and bone that surround the teeth — affects about 47% of adults in the United States and is common in people with rheumatoid arthritis, an autoimmune disease that mainly attacks the joints. New research suggests that in people with rheumatoid arthritis and periodontal disease, oral bacteria can break into the bloodstream and trigger inflammation from immune cells. 

The findings, published in Science Translational Medicine, indicate that periodontal disease may contribute to rheumatoid arthritis by triggering specific immune responses. “Future studies are needed to determine whether improved oral care may provide therapeutic benefit in the management of [rheumatoid arthritis],” the researchers say. 

People with rheumatoid arthritis have an unusually high risk of periodontal disease, which can damage the oral mucosa, allowing mouth bacteria to enter the bloodstream. Scientists have suspected that periodontal disease could trigger inflammation elsewhere in the body, but the mechanisms behind this process have remained mysterious. 

To investigate the link between the two conditions, a team of researchers led by William Robinson at Stanford University and Dana Orange at Rockefeller University analyzed blood samples and joint fluids from people with rheumatoid arthritis with and without periodontal disease.

Flare-up trigger

The researchers collected blood samples from five women with rheumatoid arthritis, some of whom had periodontal disease, each week over the course of one to four years. The team also examined joint fluids and plasma samples from two other groups of people with rheumatoid arthritis.

Compared to people without periodontal disease, those with the condition had higher levels of oral bacteria in their blood. The most common bacteria were Streptococcus species, which were also the most abundant microbes detected in mouth swabs.

People with periodontal disease also experienced repeated flare-ups of rheumatoid arthritis, the researchers found. The presence of oral bacteria in blood appeared to trigger the activation of immune responses in people with periodontal disease and rheumatoid arthritis flare-ups.

Contributing to inflammation

Further experiments suggested that oral bacteria in the blood activated specific immune cells, which released a type of antibodies that are commonly observed in people with rheumatoid arthritis. These antibodies, known as anti-citrullinated protein antibodies (ACPAs), are directed against an individual’s own proteins that have undergone citrullination — a reaction that converts the amino acid arginine into the amino acid citrulline.

The researchers discovered that bacteria enriched in the mouth of people with periodontal disease are highly citrullinated. The team also found that ACPAs bind citrullinated bacterial peptides from people with periodontal disease.

These and other experiments, the researchers say, “indicate that patients develop antibodies that bind citrullinated oral commensal bacterial proteins that are cross-reactive against known human citrullinated autoantigens.” 

The findings also suggest, they add, “that [periodontal disease], through repeated mucosal breaks, results in recurring innate and adaptive immune activation that may contribute to the pathogenesis of [rheumatoid arthritis].”

What this research adds
The review aims to gather 26 studies focused on the relationship between the oral cavity microbiome and Alzheimer’s disease through a genetic approach.

Conclusions
Periodontitis might be associated with cognitive decline, suggesting a possible etiopathologic role in Alzheimer’s disease. However, the contribution of the microbiome remains uncertain, pending more targeted and large-scale studies.

The periodontal infection known as periodontitis could be a predisposing factor for Alzheimer’s disease, since it is associated with cognitive decline. However, its role remains to be further investigated, although the oral microbiota is altered in the presence of the disease.

This is the conclusion of a review conducted by Samantha Mao of Sijhih Cathay General Hospital in New Taipei, Taiwan, published in the Journal of Dental Sciences.

Alzheimer’s disease and oral and gut microbiota

There are more than 55 million cases of Alzheimer‘s disease worldwide, accounting for 50-60% of all dementia cases. 

Multiple risk factors certainly include age and genetic predisposition, but also concomitant inflammatory and/or immune-based diseases. 

In addition, the hypothesis has recently been compounded that an altered gut microbiome, going to insist on the gut-brain axis, may play a role in the etiopathogenesis of the disease.

Indeed, bacterial metabolites such as LPS or short-chain fatty acids (SCFAs) have been shown to be able to modulate the central and peripheral nervous system by acting as a potential pathogenic link between the gut microbiota and amyloid plaque deposition typical of AD. 

The gut microbiota is not the only one involved. In fact, the oral cavity is the second largest distribution of microorganisms, after the intestine (for that matter). 

The axis thus, extends becoming gut-mouth-brain going to support the possible association between local and brain-level pathologies.

Periodontitis, an inflammation affecting the gums, has shown some correlation with Alzheimer’s disease especially in more advanced stages, where there is a generalized involvement of the immune system associated with local dysbiosis. 

Therefore, in this review, genetic studies (next-generation-sequencing; n= 26) were selected with the aim of exploring the relationship between oral microbiome and Alzheimer’s disease. Let us have a look at the outcomes.

What does emerge from the studies?

Starting from database or questionnaire-based studies (n=6), it was demonstrated that:

• Campylobacter rectus and P. gingivalis showed an association with elevated risk of Alzheimer’s disease. Similar trend for patients with high levels of antibodies to Actinomyces naeslundii.
• P. gingivalis, Prevotella melaninogenenica, Streptococcus oralis, and Staphylococcus intermedius, on the other hand, showed a correlation with increased risk of mortality.
• The gingival pathogens P. intermedia, C. rectus, P. nigrescens, P. melaninogenenica, and P. gingivalis would appear to interact synergistically with H. pylori in promoting the incidence of Alzheimer’s disease.
• High antibodies against Eubacterium nodatum would instead appear to decrease the risk of incurring Alzheimer’s disease.

However, other studies have focused on the connection between Alzheimer’s disease and clinical periodontal and serum parameters:

• Worse cognitive status showed, in general, association with tooth loss and alveolar bed issues.
• Sochocka et al. showed that inflammatory markers such as IL-1β, IL-6, IL-10, and TNF-ⲁ were higher in subjects with Alzheimer’s than in healthy controls. Normal levels of IL-1β and IL-6 for Ide et al., instead.
• Patients with Alzheimer’s disease, albeit with some conflicting results, would seem to be more likely to express IgG antibodies against Aggregatibacter actinomycetemcomitans, P. gingivalis, and Tannerella forsythia.
• Significantly higher levels of P. intermedia and Fusobacterium nucleatum in patients even before actual diagnosis.

Microbiological data, on the other hand, show that:

• Periodontal pathogens P. gingivalis and T. denticola are associated with Alzheimer’s according to Leblhuber et al. No association instead for Laugisch et al.
• Alzheimer’s patients have higher bacterial diversity according to Holmer et al., no significant difference instead for other studies.
• Actinomyces and Rothia are the most abundant species in healthy controls, Slackia exigua, Lachnospiraceae and Prevotella oulorum in the Alzheimer’s group (Holmer et al.); Wu et al. shows instead that Firmicutes, Lactobacillales, Actinomycetales and Veillonellales are the most expressed in subjects with moderate cognitive impairment (MCI), Fusobacteria, Bacteroidetes and Cardiobacteriales in controls; for Yang et al., Pasteurellaceae characterizes instead the MCI group, Lautropia mirabilis the controls; Liu et al. finally demonstrates a predominance of Moraxella, Leptotrichia and Sphaerochaeta in AD patients, Rothia in controls.

In conclusion, postmortem studies show:

• Actinobacteria are more present in the Alzheimer’s group, Proteobacteria in controls.
• P. gingivalis and Treponema were found to be present in the brain of Alzheimer’s patients, not in controls

Conclusions

To sum up, epidemiological-database and postmortem studies seem to confirm an association between periodontal pathogens and Alzheimer’s disease. However, there are incongruent results with genetic data, probably due to a different protocol for sampling and parameters for assessing cognitive status. 

Also, it is important to consider how the controls included in these studies may already have undiagnosed cognitive decline. Further studies, including those providing periodontal treatment, are therefore needed to better clarify the relationship between Alzheimer’s to periodontal diseases.

What is already known on this topic
The mouth is home to many microbial species that colonize our respiratory and digestive tracts. Smoking traditional cigarettes is known to create an environment in which certain bacteria can grow and trigger gum disease and infection, but it’s unclear how e-cigarettes affect the mouth microbiota.

What this research adds
By studying the mouth microbiota of nearly 120 individuals, including e-cigarette users and regular cigarette smokers, researchers found that about 72% of cigarette smokers and nearly 43% of e-cigarette users suffered from gum disease and infection. E-cigarette users had an altered mouth microbiota, which was associated with high levels of immune molecules involved in inflammation. E-cigarette vapors also made cells grown in a lab dish more prone to bacterial infection.

Conclusion
The findings suggest that e-cigarette users are at greater risk for mouth infections than non-smokers.

Smoking e-cigarettes is often considered safer than regular smoking, but the unknowns about vaping are many, including how it affects people’s health in the long term. Now researchers have found that smoking e-cigarettes changes the community of microbes living in the mouth, making users prone to inflammation and infection.

The study, published in iScience, is the first to show that vaping alters the mouth’s microbiota. “Given the popularity of vaping, it is critical that we learn more about the effects of e-cigarette aerosols on the oral microbiome and host inflammatory responses in order to better understand the impact of vaping on human health,” says study’s co-senior author Xin Li, a researcher at New York University College of Dentistry.

The mouth is home to many microbial species that colonize our respiratory and digestive tracts. Smoking traditional cigarettes is known to create an environment in which certain bacteria can grow and trigger gum disease and infection, but it’s unclear how e-cigarettes, which are becoming increasingly popular among teens, affect the mouth microbiota.

To address this question, Li and her team studied the mouth microbiota of 119 individuals: 40 regular cigarette smokers, 40 e-cigarette users, and 39 people who had never smoked.

Vaping shift

About 72% of cigarette smokers and nearly 43% of e-cigarette users suffered from gum disease and infection, compared to 28% of non-smokers. The mouth microbiota in the three groups was dominated by Streptococcus, Veillonella, Prevotella, Neisseria, Haemophilus, Porphyromonas, Rothia, and Fusobacterium, but cigarette and e-cigarette smokers tended to have different mouth bacteria than people who never smoked.

Smokers had higher levels of Actinobacteria in their saliva than non-smokers. The levels of Firmicutes and Spirochaetes were elevated in traditional cigarette smokers, while the saliva of e-cigarette smokers harbored high levels of bacteria such as Neisseria, Porphyromonas, Haemophilus, and Fusobacteria, which are often responsible for gum disease and infection. “The predominance of these periodontal pathogens in the mouths of e-cigarette users and traditional smokers is a reflection of compromised periodontal health,” Li says.

Immune effects

Many of the bacteria found in the saliva of e-cigarette smokers were associated with immune molecules involved in inflammation. So, to further test the effects of vaping on inflammatory responses, the researchers exposed cells grown in a lab dish to e-cigarette vapors and then infected the cells with the gum pathogens Porphyromonas gingivalis and Fusobacterium nucleatum.

After being exposed to e-cigarette vapors, the cells had increased levels of pro-inflammatory molecules and became more prone to bacterial infection.

The findings suggest that vaping can put users at risk for gum disease and infection, the researchers say. However, the team cautions that all the experiments were done in cell grown in a dish, so future studies should validate the findings in 3D oral tissue models to identify the health effects of e-cigarette vapors and its components.

What is already known on this topic
Dental caries is a common chronic disease of childhood, caused by the interaction of oral bacteria with sugary foods. Especially amongst young children in disadvantaged families, caries is difficult to diagnose at early stages causing irreversible cavitation. Cross-sectional studies have previously associated Streptococcus mutants to early childhood caries.

What this research adds
Using a longitudinal cohort study model, the researchers characterized for the first time the development of the oral microbiome in 134 children during the first four years of life, correlating it with dental caries clinical manifestations. The study identified the abundance of certain bacterial oral species in children before caries formation.

Conclusion
The findings suggest that saliva testing early in life could flag caries development in children, offering the opportunity to treatment before cavitation occurs.

S. G. Dashper and collaborators at University of Melbourne in Australia, have identified oral bacterial species that are elevated in children before they develop dental caries. Such microbes could serve as biomarkers for monitoring teeth health and to prevent irreversible caries complications. 

Early childhood caries (ECC) is a major oral health problem, more frequent in socially disadvantaged populations and associated with frequent dietary carbohydrate intake.

ECC are preventable, however can be difficult to make a diagnosis before clinical manifestation. Cavitation is a late stage complication of ECC and it starts with dysbiotic changes in the species composition of the supragingival plaque microbiota that shifts from the commensal plaque biofilm, to a microbiome community enriched in acidogenic and aciduric species, as shown by previous cross-sectional studies.

The appearance of ECC has been associated to the abundance of the bacteria Streptococcus mutans and Streptococcus sobrinus in the supragingival plaque. Therefore, detection of changes in composition of the supragingival plaque microbiota could help detecting ECC before clinical complications onset.

Using a longitudinal cohort analysis model, this study characterized the oral microbiome development of 134 children over time, at six time-points from two months-of-age to four years-of-age, while monitoring oral health state. The microbiomes of their mothers was also analyzed at a single time point. 

The researchers sequenced the oral microbiome from saliva DNA samples and identified bacterial species as Operational Taxonomic Units (OTUs), founding that: 

• Saliva of infants showed an increase in the mean number of bacteria over time, from 7 bacterial OTUs at 1.9 months-of-age, to 28 bacterial OTUs at 13.2 months-of-age, together with a distinct shift in the composition of the microbiome. 
• Five most abundant bacterial species, Streptococcus mitis group, G. haemolysans, S. salivarius group, H. parainfluenzae, and Granulicatella elegans, represented 90% of the total bacteria found in saliva at 1.9 months-of-age. This proportion declined to 70% by four years-of-age. 
• The mothers had a distinct oral microbiome from the children with a microbiome of 54 species, in which S. salivarius was the major component. Its abundance was similar in infants only at two months-of-age.

After characterizing the temporal development of the oral microbiome in children, to monitor the formation and severity of ECC, the team applied the modified International Caries Detection and Assessment System (ICDAS II) criteria to their investigation. Then, combining statistical analysis to ICDAS II they identify bacterial species associated with disease development. 

Considering specific time points, the clinical assessment revealed that all infants examined in this study had a healthy dentition up to 19.7 months and later on the severity of ECC varied across children with disease. There researchers also found that: 

• In the youngest children at 1.9, 7.7 and 19.7 months-of-age, there were no significant differences in the oral microbiome. 
• From 39 to 48.6 months-of-age instead, particular taxa in the oral microbiome such as S. mutans, S. sobrinus and V. parvula, were significantly more abundant in children who developed disease compared with those who remained healthy at 48.6 months-of-age. 
• Leptotrichia shahii, Scardovia wiggsiae and Leptotrichia IK040 were also associated with disease but only at 48.6 months-of-age.
• On the contrary, other species including Prevotella nigrescens, three species of Leptotricia and Actinobaculum 12B759 levels were decreased in children who developed disease. 
• Interestingly, at 48.6 months-of-age the prevalence of S. mutans started decreasing and children who developed disease at 60 months-of-age, compared with the healthy group had no differences in the levels of S. mutans. 

The research data showed that S. mutans was the most discriminatory taxa for the prediction of clinical disease, suggesting that this species could serve as a biomarker in early years of life, up to 48.6 months-of-age. Therefore, the researchers did more investigations on S. mutans, revealing that there was no significant difference in its abundance in the saliva of the mothers whose children remained healthy and those who developed clinical signs of disease at late time points. Thus, the abundance of S. mutans was not predictive of ECC in their children. 

In summary this study shows that while the oral microbiome follows an ordered and sequential development pattern during the first four years of life, elevated levels of S. mutans, S. sobrinus and V. parvula in saliva could work as markers for childhood oral dysbiosis. Identifying certain microbiome populations on time, would help preventing ECC irreversible clinical manifestations, especially in children at highest risk of dental disease.