Cross-Cohort Microbiome Analysis of Salivary Biomarkers in Patients With Type 2 Diabetes Mellitus

Exploring oral microbiome in oral squamous cell carcinoma across environment-associated sample types

The relationship between the oral microbiome and oral squamous cell carcinoma (OSCC) has been extensively investigated. Nonetheless, most previous studies were single-center, resulting in the absence of systematic evaluations. To address this gap, we performed a comprehensive meta-analysis on 1,255 samples from OSCC-related 16S rRNA gene data sets, representing a diverse range of OSCC phenotypes. It is recognized that the progression of cancer is related to the alterations in the microbiome among different phenotypes. Our findings revealed distinct microbiome characteristics among different sample types, with Biopsy (Bios) and Swab samples exhibiting significant differences between phenotypes. In Bios samples, the microbiomes of the Cancer group and the normal tissue adjacent to the tumor (NAT) group display a higher similarity, while both differ from the microbiome of the Fibroepithelial polyp (FEP) group. Moreover, the identified differential genera and pathways corresponded with these observations. We developed a diagnostic model using the random forest algorithm on Swab samples, achieving an area under the receiver operating characteristic curve (AUC) of 0.918. Importantly, this model exhibited considerable effectiveness (AUC = 0.849) when applied to another sequencing platform. Taken together, our study provides a comprehensive overview of the oral microbiome during various OSCC progression stages, potentially enhancing early detection and treatment.IMPORTANCEThis study answers key questions regarding the universal microbial characteristics and comprehensive oral microbiome dynamics during oral squamous cell carcinoma (OSCC) progression. By integrating multiple data sets, we examine the following critical aspects: (1) Do different sample types harbor distinct microbial communities within the oral cavity? (2) Which sample types offer greater potential for investigating OSCC progression? (3) How are the oral microbiomes of the Cancer group, normal tissue adjacent to the tumor group, and Fibroepithelial polyp group related, and what is their potential association with OSCC development? (4) Can a diagnostic model based on microbial signatures effectively distinguish between Cancer and Health groups using Swab samples?

Salivary proteomics and metaproteomics identifies distinct molecular and taxonomic signatures of type-2 diabetes

BACKGROUND

Saliva is a protein-rich body fluid for noninvasive discovery of biomolecules, containing both human and microbial components, associated with various chronic diseases. Type-2 diabetes (T2D) imposes a significant health and socio-economic burden. Prior research on T2D salivary microbiome utilized methods such as metagenomics, metatranscriptomics, 16S rRNA sequencing, and low-throughput proteomics.

RESULTS

We conducted ultrafast, in-depth MS-based proteomic and metaproteomic profiling of saliva from 15 newly diagnosed T2D individuals and 15 age-/BMI-matched healthy controls (HC). Using state-of-the-art proteomics, over 4500 human and bacterial proteins were identified in a single 21-min run. Bioinformatic analysis revealed host signatures of altered immune-, lipid-, and glucose-metabolism regulatory systems, increased oxidative stress, and possible precancerous changes in T2D saliva. Abundance of peptides for bacterial genera such as Neisseria and Corynebacterium were altered showing biomarker potential, offering insights into disease pathophysiology and microbial applications for T2D management.

CONCLUSIONS

This study presents a comprehensive mapping of salivary proteins and microbial communities, serving as a foundational resource for enhancing understanding of T2D pathophysiology. The identified biomarkers hold promise for advancing diagnostics and therapeutic approaches in T2D and its associated long-term complication Video Abstract.

The Role of Oral Biomarkers in the Assessment of Noncommunicable Diseases

Background/Objectives: Oral biomarkers have gained attention as non-invasive tools for assessing systemic diseases due to their potential to reflect physiological and pathological conditions. This review aims to explore the role of oral biomarkers in diagnosing and monitoring systemic diseases, emphasizing their diagnostic relevance and predictive capabilities in clinical practice. Methods: This narrative review synthesizes the current literature on biochemical, immunological, genetic, and microbiological oral biomarkers, with a focus on their sources, types, and clinical applications. Key studies were analyzed to identify associations between oral biomarkers and systemic diseases such as cardiovascular diseases, type 2 diabetes mellitus, autoimmune disorders, and cancers. Results: Oral fluids, including saliva and gingival crevicular fluid, contain diverse biomarkers such as matrix metalloproteinases, cytokines, and genetic indicators. These markers have demonstrated potential in diagnosing and monitoring systemic conditions. Among others, elevated levels of salivary glucose and inflammatory cytokines correlate with diabetes progression, while vascular endothelial growth factor (VEGF) and salivary C-reactive protein might be applicable as indicators for periodontal disease and cardiovascular risk. Additionally, salivary biomarkers like amyloid-beta and tau are promising in detecting neurodegenerative disorders. Conclusions: Oral biomarkers might represent a transformative and point-of-care approach to the early management of systemic diseases; however, challenges in measurement variability, standardization, and validation remain.

A universal oral microbiome-based signature for periodontitis

We analyzed eight oral microbiota shotgun metagenomic sequencing cohorts from five countries and three continents, identifying 54 species biomarkers and 26 metabolic biomarkers consistently altered in health and disease states across three or more cohorts. Additionally, machine learning models based on taxonomic profiles achieved high accuracy in distinguishing periodontitis patients from controls (internal and external areas under the receiver operating characteristic curves of 0.86 and 0.85, respectively). These results support metagenome-based diagnosis of periodontitis and provide a foundation for further research and effective treatment strategies.

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.