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Salivary Multi-omics Characterization and Staging Diagnostic Model for Type 2 Diabetes with Different Stages of Periodontitis

ABSTRACT:

Background: This study aims to comprehensively characterize the dynamic succession of the salivary microbiome and metabolic reprogramming in type 2 diabetes mellitus (T2DM) patients across progressive stages of periodontitis (Stage I–IV) using an integrated multi-omics approach. We specifically sought to evaluate the clinical value of a salivary multi-omics-based machine learning model for the precision stratification of periodontitis severity.

Methods: Unstimulated saliva samples were collected from 120 T2DM patients stratified into four groups based on periodontitis severity (Stages I–IV, n=30 per group). 16S rDNA amplicon sequencing and LC-MS untargeted metabolomics were employed to analyze microbial and metabolic profiles. Multivariate statistics, Procrustes analysis, and Random Forest algorithms were utilized to identify key features and construct diagnostic models.

Results: Disease progression was characterized by a significant restructuring of the microbiome, featuring an increase in Firmicutes and enrichment of core pathogens (e.g., Porphyromonas, Treponema) in Stage IV. Metabolically, a stage-specific shift was observed from early amino acid perturbations to late-stage active proteolysis (accumulation of Pro-Pro dipeptides) and inflammatory lipid dysregulation. Notably, the abundance of Lactobacillus was significantly positively correlated with HbA1c levels and periodontal destruction. The Random Forest model based on these multi-omics signatures achieved an AUC of 0.961 for identifying Stage IV periodontitis.

Conclusion: This study reveals that the progression of periodontitis in T2DM patients is associated with specific remodeling of the salivary microecosystem, mechanistically driven by pathogen-induced proteolysis and dysregulated inflammatory lipid metabolism. The significant correlation between Lactobacillus and HbA1c provides novel evidence for the oral-systemic metabolic interplay. Furthermore, the high-accuracy diagnostic model validates the potential of saliva for non-invasive disease stratification.

INSTRUMENT(S): Liquid Chromatography MS - negative - reverse-phase, Liquid Chromatography MS - positive - reverse-phase

PROVIDER: MTBLS13827 | MetaboLights | 2026-05-20

REPOSITORIES: MetaboLights

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			Action	DRS
	a_MTBLS13827_LC-MS_negative_reverse-phase_metabolite_profiling.txt	Txt
	a_MTBLS13827_LC-MS_positive_reverse-phase_metabolite_profiling.txt	Txt
	i_Investigation.txt	Txt
	m_MTBLS13827_LC-MS_negative_reverse-phase_metabolite_profiling_v2_maf.tsv	Tabular
	m_MTBLS13827_LC-MS_positive_reverse-phase_metabolite_profiling_v2_maf.tsv	Tabular

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Project description:Pancreatic cancer is the fourth leading cause of cancer death. Lack of early detection technology for pancreatic cancer invariably leads to a typical clinical presentation of incurable disease at initial diagnosis. Oral fluid (saliva) meets the demand for non-invasive, accessible, and highly efficient diagnostic medium. The level of salivary analytes, such as mRNA and microflora, vary upon disease onset; thus possess valuable signatures for early detection and screening. In this study, we evaluated the performance and translational utilities of the salivary transcriptomic and microbial biomarkers for non-invasive detection of early pancreatic cancer. Two biomarker discovery technologies were used to profile transcriptome in saliva supernatant and microflora in saliva pellet. The Affymetrix Human Genome U133 Plus 2.0 Array was used to discover altered gene expression in saliva supernatant. The Human Oral Microbe Identification Microarray (HOMIM) was used to investigate microflora shift in saliva pellet. Biomarkers selected from both studies were subjected to an independent clinical validation using a cohort of 30 early pancreatic cancer, 30 chronic pancreatitis and 30 healthy matched-control saliva samples. Two panels of salivary biomarkers, including eleven mRNA biomarkers and two microbial biomarkers were discovered and validated for pancreatic cancer detection. The logistic regression model with the combination of three mRNA biomarkers (ACRV1, DMXL2 and DPM1) yielded a ROC-plot AUC value of 0.974 (95% CI, 0.896 to 0.997; P < 0.0001) with 93.3% sensitivity and 90% specificity in distinguishing pancreatic cancer patients from healthy subjects. The logistic regression model with the combination of two bacterial biomarkers (Neisseria elongata and Streptococcus mitis) yielded a ROC-plot AUC value of 0.895 (95% CI, 0.784 to 0.961; P < 0.0001) with 96.4% sensitivity and 82.1% specificity in distinguishing pancreatic cancer patients from healthy subjects. Importantly, the logistic regression model with the combination of four biomarkers (mRNA biomarkers, ACRV1, DMXL2 and DPM1; bacterial biomarker, S. mitis) could differentiate pancreatic cancer patients from all non-cancer subjects (chronic pancreatitis and healthy control), yielding a ROC-plot AUC value of 0.949 (95% CI, 0.877 to 0.985; P < 0.0001) with 92.9% sensitivity and 85.5% specificity. This study comprehensively compared the salivary transcriptome and microflora between pancreatic cancer and control subjects. We have discovered and validated eleven mRNA biomarkers and two microbial biomarkers for early detection of pancreatic cancer in saliva. The logistic regression model with four salivary biomarkers can detect pancreatic cancer specifically without the complication of chronic pancreatitis. This is the first report demonstrating the value of multiplex salivary biomarkers for the non-invasive detection of a high impact systemic cancer. Keywords: Salivary biomarker, pancreatic cancer, early detection, salivary transcriptome, salivary microflora This study, which was approved by the UCLA Institutional Review Board, started sample collection in February 2006 and ended in April 2008. It had a discovery and verification phase, followed by an independent validation phase. All patients with clinically diagnosed early or locally advanced pancreatic cancer, chronic pancreatitis and matched healthy control were recruited from the UCLA Medical Center. The controls were matched for gender, age, ethnicity and smoking history. We chose early and locally advanced pancreatic cancers because they were considered early stage diseases. All patients were recently diagnosed with primary disease, and had not received any prior treatment in the form of chemotherapy, radiotherapy, surgery, or alternative remedies. No subjects had a history of prior malignancy, immunodeficiency, autoimmune disorders, hepatitis, or HIV infection. Written informed consent was obtained from all patients who agreed to serve as saliva donors. Unstimulated saliva samples were collected and processed as previously described. The saliva bank of pancreatic disease at the UCLA Dental Research Institute has collected 283 saliva samples since 2006. Of these, 114 samples, from 42 pancreatic cancer patients, 30 chronic pancreatitis patients and 42 healthy control subjects, met the following eligibility criteria: the disease subjectsâ?? samples were obtained immediately after primary diagnosis from patients without evidence of metastasis; the saliva sample was free of blood (Table 1 in accompanying manuscript). Of the 114 samples, 12 pancreatic cancer samples and 12 healthy control samples were chosen for the discovery and verification phase. The transcriptomic approach profiled the saliva supernatant samples from 12 pancreatic cancer patients and 12 healthy control subjects using the Affymetrix Human Genome U133 Plus 2.0 Array platform.

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Salivary Multi-omics Characterization and Staging Diagnostic Model for Type 2 Diabetes with Different Stages of Periodontitis

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