Characterization of Salivary Microbiota in Patients with Atherosclerotic Cardiovascular Disease: A Case-Control Study

Microbiota of Saliva: A Non-invasive Diagnostic Tool

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.

Changes in salivary microbiota due to gastric cancer resection and its relation to gastric fluid microbiota

Gastric cancer is one of the leading causes of death worldwide, and resections are performed to cure the disease. We have previously reported the changes in the gastric microbiota after gastric cancer resection, which may be associated with the oral microbiota; however, the changes in the oral microbiota remain uncharacterized. This study aimed to characterize the changes in the salivary microbiota caused by gastric cancer resection and to evaluate their association with the gastric fluid microbiota. Saliva and gastric fluid samples were collected from 63 patients who underwent gastrectomy before and after surgery, and a 16S rRNA metagenomic analysis was performed to compare the microbiota composition. The number of bacterial species in the salivary microbiota decreased, and the bacterial composition changed after the resection of gastric cancer. In addition, we identified several bacterial genera that varied significantly in the salivary microbiota, some of which also showed similar changes in the gastric fluid microbiota. These findings indicate that changes in the gastric environment affect the oral microbiota, emphasizing the close association between the oral and gastric fluid microbiota. Our study signifies the importance of focusing on the oral microbiota in the perioperative period of gastrectomy in patients with gastric cancer.

Probing the Relationship Between the Human Gut Microbiome and Prospects of Prostate Cancer: A Systematic Review

Prostate neoplasia is one of the most commonly occurring neoplasias in males and has a high mortality rate. Prostate cancer (PCA) risk factors include tall stature, male sex, known family history, obesity, high blood pressure, lack of fitness, higher levels of testosterone for a long time, increasing age, and ethnicity are well known. The association and role of the gut microbiota in different diseases in our body have been highlighted recently. Therefore, finding the influence of gut microbiota on the prostatic cells can be useful for preventing prostatic neoplasia and/or reducing its severity. We aimed to assess its impact on PCA risk. We thoroughly searched databases for the relevant literature for our systematic review. The final research papers analyzed how bacteria played a role in the risk of PCA, either through inflammation or the production of metabolites that increase/decrease the risk of PCA. Based on the studies reviewed, we found that some gut bacteria play a role in the formation of PCA. In contrast, some bacteria can help prevent PCA, but the metabolism of the dietary components is the major factor for PCA.

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.

Comparative characterization of inflammatory profile and oral microbiome according to an inflammation-based risk score in ST-segment elevation myocardial infarction

Ischemic heart disease considers the myocardial infarction (MI), either non-ST-segment elevation (non-STEMI) or ST-segment elevation myocardial infarction (STEMI); this represents the main cause of mortality in Mexican population. Regarding to the inflammatory state, this is reported to be a major prognostic factor of mortality for patients with MI. One of the conditions capable of producing systemic inflammation is periodontal disease. It has been proposed that the oral microbiota is translocated through the bloodstream to the liver and intestine, generating intestinal dysbiosis. The aim of this protocol is to assess oral microbiota diversity and circulating inflammatory profile in STEMI patients stratified according to an inflammation-based risk scoring system. We found that Bacteriodetes phylum was the most abundant in STEMI patients, and Prevotella was the most abundant genus, with a higher proportion in periodontitis patients. In fact, Prevotella genus was found to correlate positively and significantly with elevated IL-6 concentration. Our study defined a non-causal association inferred between the cardiovascular risk of STEMI patients, determined by changes in the oral microbiota that influence the development of periodontal disease and its relationship with the exacerbation of the systemic inflammatory response.

Characterization of Oral Microbiota Following Chemotherapy in Patients With Hematopoietic Malignancies

Oral microbiota may be associated with serious local or systemic medical conditions resulting from chemotherapy. This study was conducted to evaluate the changes in the oral microbiota following the initiation of chemotherapy in patients with hematopoietic malignancies and to identify the characteristics of the oral microbiota associated with oral mucositis. Oral samples were collected from 57 patients with hematopoietic malignancies at 2 time points: before the start of chemotherapy and 8 to 20 days after the start of chemotherapy, when chemotherapy-induced oral mucositis often occurs, and 16S rRNA metagenomic analyses were performed. Comparative and linear discriminant analysis effect size (LEfSe) analyses were used to determine the characteristic bacterial groups before and after the initiation of chemotherapy and in those who developed oral mucositis. The alpha and beta diversities of oral microbiota before and after the initiation of chemotherapy differed significantly (operational taxonomic unit index, P < .001; Shannon's index, P < .001; unweighted UniFrac distances, P = .001; and weighted UniFrac distances, P = .001). The LEfSe analysis revealed a group of bacteria whose abundance differed significantly before and after the initiation of chemotherapy. In the group of patients who developed oral mucositis, a characteristic group of bacteria was identified before the start of chemotherapy. In conclusion, we characterized the oral microbiota associated with the initiation of chemotherapy in patients with hematopoietic malignancies. In addition, our findings suggest that oral microbiota composition before the start of chemotherapy may be associated with oral mucositis. The results of this study emphasize the importance of oral management focusing on the oral microbiota during chemotherapy in patients with hematologic malignancies.

Salivary Microbiota Associated with Peripheral Microvascular Endothelial Dysfunction

AIMS

Oral health is associated with atherosclerotic cardiovascular disease (ACVD). We previously identified the salivary microbiota characteristics of patients with ACVD. However, whether salivary microbiota is characteristic under impaired vascular endothelial function before ACVD onset remains unclear. Therefore, we aimed to evaluate the characteristics of salivary microbiota associated with peripheral microvascular endothelial dysfunction.

METHODS

We collected saliva samples from 172 community-dwelling elderly individuals without a history of ACVD and performed 16S rRNA metagenomic analysis. We assessed the peripheral microvascular endothelial function using reactive hyperemia index (RHI) and compared the salivary microbiota in the groups with normal (RHI ≥ 2.10), borderline, and abnormal (RHI ＜1.67) peripheral endothelial function. Furthermore, we applied machine learning techniques to evaluate whether salivary microbiota could discriminate between individuals with normal and abnormal endothelial function.

RESULTS

The number of operational taxonomic units (OTUs) was higher in the abnormal group than in the normal group (p=0.037), and differences were found in the overall salivary microbiota structure (unweighted UniFrac distances, p=0.038). The linear discriminant analysis (LDA) effect size (LEfSe) algorithm revealed several significantly differentially abundant bacterial genera between the two groups. An Extra Trees classifier model was built to discriminate between groups with normal and abnormal vascular endothelial function based on the microbial composition at the genus level (AUC=0.810).

CONCLUSIONS

The salivary microbiota in individuals with endothelial dysfunction was distinct from that in individuals with normal endothelial function, indicating that the salivary microbiota may be related to endothelial function.

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.

Comparison of the gastric microbiome in Billroth I and Roux-en-Y reconstructions after distal gastrectomy

The changes in gastric microbiota following reconstruction after gastrectomy have not been reported. This study aimed to compare the gastric microbiota following Billroth I and Roux-en-Y reconstructions after distal gastrectomy. We enrolled 71 gastrectomized patients with gastric cancer; 31 and 40 underwent Billroth I and Roux-en-Y reconstructions, respectively. During upper gastrointestinal endoscopy, gastric fluid was collected immediately before and 6 months after distal gastrectomy. Deoxyribonucleic acid isolated from each sample was evaluated using 16S ribosomal ribonucleic acid metagenomic analysis. Analysis revealed that the gastric microbiota’s species richness (expressed as the alpha diversity) was significantly lower after than before distal gastrectomy (operational taxonomic units, p = 0.001; Shannon index, p = 0.03). The interindividual diversity (beta diversity) was significantly different before and after distal gastrectomy (unweighted UniFrac distances, p = 0.04; weighted UniFrac distances, p = 0.001; Bray–Curtis, p = 0.001). Alpha and beta diversity were not significantly different between Billroth I and Roux-en-Y reconstructions (observed operational taxonomic units, p = 0.58; Shannon index, p = 0.95; unweighted UniFrac distances, p = 0.65; weighted UniFrac distances, p = 0.67; Bray–Curtis, p = 0.63). Our study demonstrated significant differences in gastric microbiota diversity, composition, and community before and after distal gastrectomy but no difference between Billroth I and Roux-en-Y reconstruction after distal gastrectomy.

Can the Salivary Microbiome Predict Cardiovascular Diseases? Lessons Learned From the Qatari Population

Background: Many studies have linked dysbiosis of the gut microbiome to the development of cardiovascular diseases (CVD). However, studies assessing the association between the salivary microbiome and CVD risk on a large cohort remain sparse. This study aims to identify whether a predictive salivary microbiome signature is associated with a high risk of developing CVD in the Qatari population. Methods: Saliva samples from 2,974 Qatar Genome Project (QGP) participants were collected from Qatar Biobank (QBB). Based on the CVD score, subjects were classified into low-risk (LR < 10) (n = 2491), moderate-risk (MR = 10-20) (n = 320) and high-risk (HR > 30) (n = 163). To assess the salivary microbiome (SM) composition, 16S-rDNA libraries were sequenced and analyzed using QIIME-pipeline. Machine Learning (ML) strategies were used to identify SM-based predictors of CVD risk. Results: Firmicutes and Bacteroidetes were the predominant phyla among all the subjects included. Linear Discriminant Analysis Effect Size (LEfSe) analysis revealed that Clostridiaceae and Capnocytophaga were the most significantly abundant genera in the LR group, while Lactobacillus and Rothia were significantly abundant in the HR group. ML based prediction models revealed that Desulfobulbus, Prevotella, and Tissierellaceae were the common predictors of increased risk to CVD. Conclusion: This study identified significant differences in the SM composition in HR and LR CVD subjects. This is the first study to apply ML-based prediction modeling using the SM to predict CVD in an Arab population. More studies are required to better understand the mechanisms of how those microbes contribute to CVD.

Microbiome of Saliva and Plaque in Children According to Age and Dental Caries Experience

Dental caries are one of the chronic diseases caused by organic acids made from oral microbes. However, there was a lack of knowledge about the oral microbiome of Korean children. The aim of this study was to analyze the metagenome data of the oral microbiome obtained from Korean children and to discover bacteria highly related to dental caries with machine learning models. Saliva and plaque samples from 120 Korean children aged below 12 years were collected. Bacterial composition was identified using Illumina HiSeq sequencing based on the V3–V4 hypervariable region of the 16S rRNA gene. Ten major genera accounted for approximately 70% of the samples on average, including Streptococcus, Neisseria, Corynebacterium, and Fusobacterium. Differential abundant analyses revealed that Scardovia wiggsiae and Leptotrichia wadei were enriched in the caries samples, while Neisseria oralis was abundant in the non-caries samples of children aged below 6 years. The caries and non-caries samples of children aged 6–12 years were enriched in Streptococcus mutans and Corynebacterium durum, respectively. The machine learning models based on these differentially enriched taxa showed accuracies of up to 83%. These results confirmed significant alterations in the oral microbiome according to dental caries and age, and these differences can be used as diagnostic biomarkers.

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

The oral microbiome is receiving growing interest from the scientific community, as the mouth is the gateway for numerous potential etiopathogenetic factors in different diseases. In addition, the progression of niches from the mouth to the gut, defined as "oral-gut microbiome axis", affects several pathologies, as rheumatic diseases. Notably, rheumatic disorders (RDs) are conditions causing chronic, often intermittent pain affecting the joints or connective tissue. In this review, we examine evidence which supports a role for the oral microbiome in the etiology and progression of various RDs, including rheumatoid arthritis (RA), Sjogren's syndrome (SS), and systemic lupus erythematosus (SLE). In addition, we address the most recent studies endorsing the oral microbiome as promising diagnostic biomarkers for RDs. Lastly, we introduce the concepts of artificial intelligence (AI), in particular, machine learning (ML) and their general application for understanding the link between oral microbiota and rheumatic diseases, speculating the application of a possible AI approach-based that can be applied to personalized medicine in the future.

Characterization of salivary microbiota in elderly patients with type 2 diabetes mellitus: a matched case-control study

OBJECTIVE

The importance of oral health in type 2 diabetes mellitus (T2DM) is widely recognized; however, oral microbiota characteristics associated with T2DM in the elderly population are not well-understood. This study was conducted to evaluate the characteristics of the salivary microbiota in elderly Japanese patients with T2DM.

METHODS

Saliva samples were collected from 42 elderly Japanese patients with T2DM and 42 age- and sex-matched subjects without T2DM (control). 16S ribosomal RNA metagenomic analysis and comparative analysis of both groups were performed. Random forest classification by machine learning was performed to discriminate between the salivary microbiota in the two groups.

RESULTS

There were significant differences in the overall salivary microbiota structure between the T2DM and control groups (beta diversity; unweighted UniFrac distances, p = 0.001; weighted UniFrac distances, p = 0.001). The phylum Firmicutes was abundant in patients with T2DM, whereas the phylum Bacteroidetes was abundant in controls. The T2DM prediction model by random forest based on salivary microbiota data was verified with a high predictive potential in five cross-validation tests (area under the curve (AUC) = 0.938 (95% CI, 0.824-1.000)).

CONCLUSION

Characterization revealed that the salivary microbiota profile of the elderly patients with T2DM is significantly distinct from that of the controls.

CLINICAL RELEVANCE

These data indicate the necessity of oral health management based on the characteristics of the salivary microbiota in elderly patients with T2DM. Our findings will contribute to future research on the development of new diagnostic and therapeutic methods for this purpose.