Exploring the Oral Microbiome in Rheumatic Diseases, State of Art and Future Prospective in Personalized Medicine with an AI Approach

Personalized identification of autism-related bacteria in the gut microbiome using explainable artificial intelligence

Autism spectrum disorder (ASD) affects social interaction and communication. Emerging evidence links ASD to gut microbiome alterations, suggesting that microbial composition may play a role in the disorder. This study employs explainable artificial intelligence (XAI) to examine the contributions of individual microbial species to ASD. By using local explanation embeddings and unsupervised clustering, the research identifies distinct ASD subgroups, underscoring the disorder's heterogeneity. Specific microbial biomarkers associated with ASD are revealed, and the best classifiers achieved an AU-ROC of 0.965 ± 0.005 and an AU-PRC of 0.967 ± 0.008. The findings support the notion that gut microbiome composition varies significantly among individuals with ASD. This work's broader significance lies in its potential to inform personalized interventions, enhancing precision in ASD management and classification. These insights highlight the importance of individualized microbiome profiles for developing tailored therapeutic strategies for ASD.

Oral Microbiome: A Review of Its Impact on Oral and Systemic Health

PURPOSE OF REVIEW

This review investigates the oral microbiome's composition, functions, influencing factors, connections to oral and systemic diseases, and personalized oral care strategies.

RECENT FINDINGS

The oral microbiome is a complex ecosystem consisting of bacteria, fungi, archaea, and viruses that contribute to oral health. Various factors, such as diet, smoking, alcohol consumption, lifestyle choices, and medical conditions, can affect the balance of the oral microbiome and lead to dysbiosis, which can result in oral health issues like dental caries, gingivitis, periodontitis, oral candidiasis, and halitosis. Importantly, our review explores novel associations between the oral microbiome and systemic diseases including gastrointestinal, cardiovascular, endocrinal, and neurological conditions, autoimmune diseases, and cancer. We comprehensively review the efficacy of interventions like dental probiotics, xylitol, oral rinses, fluoride, essential oils, oil pulling, and peptides in promoting oral health by modulating the oral microbiome.

SUMMARY

This review emphasizes the critical functions of the oral microbiota in dental and overall health, providing insights into the effects of microbial imbalances on various diseases. It underlines the significant connection between the oral microbiota and general health. Furthermore, it explores the advantages of probiotics and other dental care ingredients in promoting oral health and addressing common oral issues, offering a comprehensive strategy for personalized oral care.

Role of Nonalcoholic Fatty Liver Disease in Periodontitis: A Bidirectional Relationship

Nonalcoholic fatty liver disease (NAFLD) and periodontitis share common risk factors such as obesity, insulin resistance (IR), and dyslipidemia, which contribute to systemic inflammation. It has been suggested that a bidirectional relationship exists between NAFLD and periodontitis, indicating that one condition may exacerbate the other. NAFLD is characterized by excessive fat deposition in the liver and is associated with low-grade chronic inflammation. There are several risk factors for the development of NAFLD, including gender, geriatric community, race, ethnicity, poor sleep quality and sleep deprivation, physical activity, nutritional status, dysbiosis gut microbiota, increased oxidative stress, overweight, obesity, higher body mass index (BMI), IR, type 2 diabetes mellitus (T2DM), metabolic syndrome (MetS), dyslipidemia (hypercholesterolemia), and sarcopenia (decreased skeletal muscle mass). This systemic inflammation can contribute to the progression of periodontitis by impairing immune responses and exacerbating the inflammatory processes in the periodontal tissues. Furthermore, individuals with NAFLD often exhibit altered lipid metabolism, which may affect oral microbiota composition, leading to dysbiosis and increased susceptibility to periodontal disease. Conversely, periodontitis has been linked to the progression of NAFLD through mechanisms involving systemic inflammation and oxidative stress. Chronic periodontal inflammation can release pro-inflammatory cytokines and bacterial toxins into the bloodstream, contributing to liver inflammation and exacerbating hepatic steatosis. Moreover, periodontitis-induced oxidative stress may promote hepatic lipid accumulation and IR, further aggravating NAFLD. The interplay between NAFLD and periodontitis underscores the importance of comprehensive management strategies targeting both conditions. Lifestyle modifications such as regular exercise, a healthy diet, and proper oral hygiene practices are crucial for preventing and managing these interconnected diseases. Additionally, interdisciplinary collaboration between hepatologists and periodontists is essential for optimizing patient care and improving outcomes in individuals with NAFLD and periodontitis.

Current Approaches to Prevent or Reverse Microbiome Dysbiosis in Chronic Inflammatory Rheumatic Diseases

Advances in knowledge of the microbiome and its relationship with the immune system have led to a better understanding of the pathogenesis of chronic inflammatory rheumatic diseases (CIRD). Indeed, the microbiome dysbiosis now occupies a particular place with implications for the determinism and clinical expression of CIRD, as well as the therapeutic response of affected patients. Several approaches exist to limit the impact of the microbiome during CIRD. This review aimed to present current strategies to prevent or reverse microbiome dysbiosis based on existing knowledge, in order to provide practical information to healthcare professionals treating patients suffering from CIRD.

Explainable artificial intelligence for microbiome data analysis in colorectal cancer biomarker identification

Background

Colorectal cancer (CRC) is a type of tumor caused by the uncontrolled growth of cells in the mucosa lining the last part of the intestine. Emerging evidence underscores an association between CRC and gut microbiome dysbiosis. The high mortality rate of this cancer has made it necessary to develop new early diagnostic methods. Machine learning (ML) techniques can represent a solution to evaluate the interaction between intestinal microbiota and host physiology. Through explained artificial intelligence (XAI) it is possible to evaluate the individual contributions of microbial taxonomic markers for each subject. Our work also implements the Shapley Method Additive Explanations (SHAP) algorithm to identify for each subject which parameters are important in the context of CRC.

Results

The proposed study aimed to implement an explainable artificial intelligence framework using both gut microbiota data and demographic information from subjects to classify a cohort of control subjects from those with CRC. Our analysis revealed an association between gut microbiota and this disease. We compared three machine learning algorithms, and the Random Forest (RF) algorithm emerged as the best classifier, with a precision of 0.729 ± 0.038 and an area under the Precision-Recall curve of 0.668 ± 0.016. Additionally, SHAP analysis highlighted the most crucial variables in the model's decision-making, facilitating the identification of specific bacteria linked to CRC. Our results confirmed the role of certain bacteria, such as Fusobacterium, Peptostreptococcus, and Parvimonas, whose abundance appears notably associated with the disease, as well as bacteria whose presence is linked to a non-diseased state.

Discussion

These findings emphasizes the potential of leveraging gut microbiota data within an explainable AI framework for CRC classification. The significant association observed aligns with existing knowledge. The precision exhibited by the RF algorithm reinforces its suitability for such classification tasks. The SHAP analysis not only enhanced interpretability but identified specific bacteria crucial in CRC determination. This approach opens avenues for targeted interventions based on microbial signatures. Further exploration is warranted to deepen our understanding of the intricate interplay between microbiota and health, providing insights for refined diagnostic and therapeutic strategies.

Unraveling the microbiome-metabolome nexus: a comprehensive study protocol for personalized management of Behçet's disease using explainable artificial intelligence

The presented study protocol outlines a comprehensive investigation into the interplay among the human microbiota, volatilome, and disease biomarkers, with a specific focus on Behçet's disease (BD) using methods based on explainable artificial intelligence. The protocol is structured in three phases. During the initial three-month clinical study, participants will be divided into control and experimental groups. The experimental groups will receive a soluble fiber-based dietary supplement alongside standard therapy. Data collection will encompass oral and fecal microbiota, breath samples, clinical characteristics, laboratory parameters, and dietary habits. The subsequent biological data analysis will involve gas chromatography, mass spectrometry, and metagenetic analysis to examine the volatilome and microbiota composition of salivary and fecal samples. Additionally, chemical characterization of breath samples will be performed. The third phase introduces Explainable Artificial Intelligence (XAI) for the analysis of the collected data. This novel approach aims to evaluate eubiosis and dysbiosis conditions, identify markers associated with BD, dietary habits, and the supplement. Primary objectives include establishing correlations between microbiota, volatilome, phenotypic BD characteristics, and identifying patient groups with shared features. The study aims to identify taxonomic units and metabolic markers predicting clinical outcomes, assess the supplement's impact, and investigate the relationship between dietary habits and patient outcomes. This protocol contributes to understanding the microbiome's role in health and disease and pioneers an XAI-driven approach for personalized BD management. With 70 recruited BD patients, XAI algorithms will analyze multi-modal clinical data, potentially revolutionizing BD management and paving the way for improved patient outcomes.

The differential crosstalk of the skin-gut microbiome axis as a new emerging actor in systemic sclerosis

OBJECTIVES

We characterized the microbiota in SSc, focusing on the skin-oral-gut axis and the serum and faecal free fatty acid (FFA) profile.

METHODS

Twenty-five SSc patients with ACA or anti-Scl70 autoantibodies were enrolled. The microbiota of faecal, saliva and superficial epidermal samples was assessed through next-generation sequencing analysis. GC-MS was used to quantify faecal and serum FFAs. Gastrointestinal symptoms were investigated with the University of California Los Angeles Scleroderma Clinical Trial Consortium Gastrointestinal Tract Instrument (UCLA GIT-2.0) questionnaire.

RESULTS

The ACA+ and anti-Scl70+ groups displayed different cutaneous and faecal microbiota profiles. The classes of cutaneous Sphingobacteriia and Alphaproteobacteria, the faecal phylum Lentisphaerae, the levels of the classes Lentisphaeria and Opitutae, and the genus NA-Acidaminococcaceae were significantly higher in faecal samples from the ACA+ patients than in samples from the anti-Scl70+ patients. The cutaneous Sphingobacteria and the faecal Lentisphaerae were significantly correlated (rho = 0.42; P = 0.03). A significant increase in faecal propionic acid was observed in ACA+ patients. Moreover, all levels of faecal medium-chain FFAs and hexanoic acids were significantly higher in the ACA+ group than in the anti-Scl70+ group (P < 0.05 and P < 0.001, respectively). In the ACA+ group, the analysis of the serum FFA levels showed an increasing trend in valeric acid.

CONCLUSION

Different microbiota signatures and FFA profiles were found for the two groups of patients. Despite being in different body districts, the cutaneous Sphingobacteria and faecal Lentisphaerae appear interdependent.

Harnessing the Microbiome: A Comprehensive Review on Advancing Therapeutic Strategies for Rheumatic Diseases

Rheumatic diseases are a group of disorders that affect the joints, muscles, and bones. These diseases, such as rheumatoid arthritis, lupus, and psoriatic arthritis, can cause pain, stiffness, and swelling, leading to reduced mobility and disability. Recent studies have identified the microbiome, the diverse community of microorganisms that live in and on the human body, as a potential factor in the development and progression of rheumatic diseases. Harnessing the microbiome offers a promising new avenue for developing therapeutic strategies for these debilitating conditions. There is growing interest in the role of oral and gut microbiomes in the management of rheumatoid arthritis and other autoimmune disease. Microbial metabolites have immunomodulatory properties that could be exploited for rheumatic disorders. A wide range of microorganisms are present in the oral cavity and are found to be vulnerable to the effects of the environment. The physiology and ecology of the microbiota become intimately connected with those of the host, and they critically influence the promotion of health or progression toward disease. This article aims to provide a comprehensive overview of the current state of knowledge on oral and gut microbiome and its potential future role in the management of rheumatic diseases. This article will also discuss newer treatment strategies such as bioinformatic analyses and fecal transplantation.

Interactions between systemic diseases and oral microbiota shifts in the aging community: A narrative review

As a gateway to general health and a diverse microbial habitat, the oral cavity is colonized by numerous microorganisms such as bacteria, fungi, viruses, and archaea. Oral microbiota plays an essential role in preserving oral health. Besides, the oral cavity also significantly contributes to systemic health. Physiological aging influences all body systems, including the oral microbial inhabitants. The cited effect can cause diseases by forming dysbiotic communities. Since it has been demonstrated that microbial dysbiosis could disturb the symbiosis state between the host and the resident microorganism, shifting the condition toward a more pathogenic one, this study investigated how the oral microbial shifts in aging could associate with the development or progression of systemic diseases in older adults. The current study focused on the interactions between variations in the oral microbiome and prevalent diseases in older adults, including diabetes mellitus, Sjögren's syndrome, rheumatoid arthritis, pulmonary diseases, cardiovascular diseases, oral candidiasis, Parkinson's disease, Alzheimer's disease, and glaucoma. Underlying diseases can dynamically modify the oral ecology and the composition of its resident oral microbiome. Clinical, experimental, and epidemiological research suggests the associations of systemic disorders with bacteremia and inflammation after oral microbial changes in older adults.

Gut dysbiosis in autoimmune diseases: Association with mortality

To better understand the impact of gut dysbiosis on four autoimmune diseases [Sjögren’s syndrome (SS), systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), and multiple sclerosis (MS)], this review investigated the altered gut bacteria in each disease and the shared ones among the four diseases. The enriched gut bacteria shared by three of the four autoimmune diseases were Streptococcus, Prevotella, and Eggerthella, which are associated with autoantibody production or activation of Th17 cells in immune-related diseases. On the other hand, Faecalibacterium comprises depleted gut bacteria shared by patients with SLE, MS, and SS, which is associated with various anti-inflammatory activities. The indexes of gut dysbiosis, defined as the number of altered gut bacterial taxa divided by the number of studies in SLE, MS, RA, and SS, were 1.7, 1.8, 0.7, and 1.3, respectively. Interestingly, these values presented a positive correlation trend with the standardized mortality rates —2.66, 2.89, 1.54, and 1.41, respectively. In addition, shared altered gut bacteria among the autoimmune diseases may correlate with the prevalence of polyautoimmunity in patients with SLE, SS, RA, and MS, that is, 41 percent, 32.6 percent, 14 percent, and 1–16.6 percent, respectively. Overall, this review suggests that gut dysbiosis in autoimmune diseases may be closely related to the failure of the gut immune system to maintain homeostasis.

Electrochemical (Bio)Sensing Devices for Human-Microbiome-Related Biomarkers

The study of the human microbiome is a multidisciplinary area ranging from the field of technology to that of personalized medicine. The possibility of using microbiota biomarkers to improve the diagnosis and monitoring of diseases (e.g., cancer), health conditions (e.g., obesity) or relevant processes (e.g., aging) has raised great expectations, also in the field of bioelectroanalytical chemistry. The well-known advantages of electrochemical biosensors-high sensitivity, fast response, and the possibility of miniaturization, together with the potential for new nanomaterials to improve their design and performance-position them as unique tools to provide a better understanding of the entities of the human microbiome and raise the prospect of huge and important developments in the coming years. This review article compiles recent applications of electrochemical (bio)sensors for monitoring microbial metabolites and disease biomarkers related to different types of human microbiome, with a special focus on the gastrointestinal microbiome. Examples of electrochemical devices applied to real samples are critically discussed, as well as challenges to be faced and where future developments are expected to go.

Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review

Investigation of the potential applications of artificial intelligence (AI), including machine learning (ML) and deep learning (DL) techniques, is an exponentially growing field in medicine and healthcare. These methods can be critical in providing high-quality care to patients with chronic rheumatological diseases lacking an optimal treatment, like rheumatoid arthritis (RA), which is the second most prevalent autoimmune disease. Herein, following reviewing the basic concepts of AI, we summarize the advances in its applications in RA clinical practice and research. We provide directions for future investigations in this field after reviewing the current knowledge gaps and technical and ethical challenges in applying AI. Automated models have been largely used to improve RA diagnosis since the early 2000s, and they have used a wide variety of techniques, e.g., support vector machine, random forest, and artificial neural networks. AI algorithms can facilitate screening and identification of susceptible groups, diagnosis using omics, imaging, clinical, and sensor data, patient detection within electronic health record (EHR), i.e., phenotyping, treatment response assessment, monitoring disease course, determining prognosis, novel drug discovery, and enhancing basic science research. They can also aid in risk assessment for incidence of comorbidities, e.g., cardiovascular diseases, in patients with RA. However, the proposed models may vary significantly in their performance and reliability. Despite the promising results achieved by AI models in enhancing early diagnosis and management of patients with RA, they are not fully ready to be incorporated into clinical practice. Future investigations are required to ensure development of reliable and generalizable algorithms while they carefully look for any potential source of bias or misconduct. We showed that a growing body of evidence supports the potential role of AI in revolutionizing screening, diagnosis, and management of patients with RA. However, multiple obstacles hinder clinical applications of AI models. Incorporating the machine and/or deep learning algorithms into real-world settings would be a key step in the progress of AI in medicine.

Oral dysbiosis and systemic diseases

The aim of this mini review is to investigate the connection between oral microbiome dysbiosis and systemic diseases. Many systemic conditions can have oral manifestations and cause worsening in oral diseases. For example, uncontrolled type 2 diabetes has been associated with worsening of periodontal disease. Other inflammatory diseases or autoimmune diseases may predispose to oral mucositis, mucosal ulcers, xerostomia, and higher susceptibility to oral infections. This review will outline common systemic diseases, such as metabolic, cardiovascular, and immunologic disorders as they relate to oral manifestations and changes in the oral microbiome composition.

The Microbiome Revolution: New Insights for Personalized Medicine

The availability of new culture-independent techniques to study microbes led to the explosion of the gut microbiota revolution in recent decades [...]

Exosome-Derived microRNAs from Mouthrinse Have the Potential to Be Novel Biomarkers for Sjögren Syndrome

Sjögren syndrome (SS) is diagnosed based on invasive tissue biopsies and blood sampling. Therefore, a novel non-invasive and simple inspection diagnostic marker of SS is required. Here, we identified exosome-derived microRNAs (miRNAs) as biomarkers for SS using non-invasive mouthrinse samples collected from patients with SS and healthy volunteers. We compared miRNAs derived from exosomes in mouthrinse samples from the two groups using microarrays and real-time polymerase chain reaction (PCR) and identified 12 miRNAs as biomarker candidates. The expression ratios of four miRNAs were significantly increased in the SS group compared to the control group. Logistic regression analysis revealed a more significant influence of miR-1290 and let-7b-5p in the SS group than that in the control group. We combined these miRNAs to create a diagnostic prediction formula using logistic regression analysis. The combination of miR-1290 and let-7b-5p distinguished SS from the control samples with an AUC, sensitivity, specificity, positive predictive value, and negative predictive value of 0.856, 91.7%, 83.3%, 84.6%, and 90.9%, respectively. These results indicated that an increased ratio of these miRNAs could serve as a novel and non-invasive diagnostic marker for SS. This is the first report of diagnosis and screening of SS by adopting a non-invasive method using mouthrinse.

Pharmacogenomics of Monoclonal Antibodies for the Treatment of Rheumatoid Arthritis

Precision medicine refers to a highly individualized and personalized approach to patient care. Pharmacogenomics is the study of how an individual’s genomic profile affects their drug response, enabling stable and effective drug selection, minimizing side effects, and maximizing therapeutic efficacy. Rheumatoid arthritis (RA) is an autoimmune disease that causes chronic inflammation in the joints. It mainly starts in peripheral joints, such as the hands and feet, and progresses to large joints, which causes joint deformation and bone damage due to inflammation of the synovial membrane. Here, we review various pharmacogenetic studies investigating the association between clinical response to monoclonal antibody therapy and their target genetic polymorphisms. Numerous papers have reported that some single nucleotide polymorphisms (SNPs) are related to the therapeutic response of several monoclonal antibody drugs including adalimumab, infliximab, rituximab, and tocilizumab, which target tumor necrosis factor (TNF), CD20 of B-cells, and interleukin (IL)-6. Additionally, there are some pharmacogenomic studies reporting on the association between the clinical response of monoclonal antibodies having various mechanisms, such as IL-1, IL-17, IL-23, granulocyte-macrophage colony-stimulating factor (GM-CSF) and the receptor activator of nuclear factor-kappa B (RANK) inhibition. Biological therapies are currently prescribed on a “trial and error” basis for RA patients. If appropriate drug treatment is not started early, joints may deform, and long-term treatment outcomes may worsen. Pharmacogenomic approaches that predict therapeutic responses for RA patients have the potential to significantly improve patient quality of life and reduce treatment costs.

The Use and Utility of Machine Learning in Achieving Precision Medicine in Systemic Sclerosis: A Narrative Review

BACKGROUND

Systemic sclerosis (SSc) is a rare connective tissue disease that can affect different organs and has extremely heterogenous presentations. This complexity makes it difficult to perform an early diagnosis and a subsequent subclassification of the disease. This hinders a personalized approach in clinical practice. In this context, machine learning (ML), a branch of artificial intelligence (AI), is able to recognize relationships in data and predict outcomes.

METHODS

Here, we performed a narrative review concerning the application of ML in SSc to define the state of art and evaluate its role in a precision medicine context.

RESULTS

Currently, ML has been used to stratify SSc patients and identify those at high risk of severe complications. Additionally, ML may be useful in the early detection of organ involvement. Furthermore, ML might have a role in target therapy approach and in predicting drug response.

CONCLUSION

Available evidence about the utility of ML in SSc is sparse but promising. Future improvements in this field could result in a big step toward precision medicine. Further research is needed to define ML application in clinical practice.

Oral Microbiota Is Associated With Immune Recovery in Human Immunodeficiency Virus-Infected Individuals

The role of the oral microbiota in HIV-infected individuals deserves attention as either HIV infection or antiretroviral therapy (ART) may have effect on the diversity and the composition of the oral microbiome. However, few studies have addressed the oral microbiota and its interplay with different immune responses to ART in HIV-infected individuals. Salivary microbiota and immune activation were studied in 30 HIV-infected immunological responders (IR) and 34 immunological non-responders (INR) (≥500 and < 200 CD4 + T-cell counts/μl after 2 years of HIV-1 viral suppression, respectively) with no comorbidities. Metagenome sequencing revealed that the IR and the INR group presented similar salivary bacterial richness and diversity. The INR group presented a significantly higher abundance of genus Selenomonas_4, while the IR group manifested higher abundances of Candidatus_Saccharimonas and norank_p_Saccharimonas. Candidatus_Saccharimonas and norank_p_Saccharimonas were positively correlated with the current CD4 + T-cells. Candidatus_Saccharimonas was positively correlated with the markers of adaptive immunity CD4 + CD57 + T-cells, while negative correlation was found between norank _p_Saccharimonas and the CD8 + CD38 + T-cells as well as the CD4/CD8 + HLADR + CD38 + T-cells. The conclusions are that the overall salivary microbiota structure was similar in the immunological responders and immunological non-responders, while there were some taxonomic differences in the salivary bacterial composition. Selenomona_4, Candidatus_Saccharimonas, and norank _p_Saccharimonas might act as important factors of the immune recovery in the immunodeficiency patients, and Candidatus_Saccharimonas could be considered in the future as screening biomarkers for the immune responses in the HIV-infected individuals.

Early-Life Adversity Leaves Its Imprint on the Oral Microbiome for More Than 20 Years and Is Associated with Long-Term Immune Changes

The early-life microbiome (ELM) interacts with the psychosocial environment, in particular during early-life adversity (ELA), defining life-long health trajectories. The ELM also plays a significant role in the maturation of the immune system. We hypothesised that, in this context, the resilience of the oral microbiomes, despite being composed of diverse and distinct communities, allows them to retain an imprint of the early environment. Using 16S amplicon sequencing on the EpiPath cohort, we demonstrate that ELA leaves an imprint on both the salivary and buccal oral microbiome 24 years after exposure to adversity. Furthermore, the changes in both communities were associated with increased activation, maturation, and senescence of both innate and adaptive immune cells, although the interaction was partly dependent on prior herpesviridae exposure and current smoking. Our data suggest the presence of multiple links between ELA, Immunosenescence, and cytotoxicity that occur through long-term changes in the microbiome.

Relationship between Saliva and Sublingual Immunotherapy

The demand for allergen specific immunotherapy (AIT), especially sublingual immunotherapy (SLIT), is increasing because of its efficacy in inducing clinical remission of allergic diseases and its low risk of side effects. Since not all patients that undergo SLIT demonstrate an improvement in allergic symptoms, the development of biomarkers to predict the outcome and adjuvants for SLIT is desired. Saliva is the first target with which tablets used in SLIT come into contact, and salivary pH, chemical properties or microbiome composition are reported to possibly be associated with the outcome of SLIT. Antibodies such as IgG4 and IgA not only in the serum but also in the saliva are increased after SLIT and may also be associated with the efficacy of SLIT. The development of the metagenomic sequencing technique makes it possible to determine the microbiome composition and ratio of each bacterium, and researchers can investigate the relationships between specific bacteria and the immune response. Some bacteria are reported to improve the SLIT outcome and have the potential to be used as biomarkers for the selection of patients and as adjuvants in SLIT. Here, we introduce biomarkers for SLIT and present recent findings regarding the relationship between saliva and SLIT.