The salivary metabolomics analyses reveal the variable metabolites in distinct staging of periodontitis

Abstract

Objective

Saliva, which is a critical component of the oral ecosystem, undergoes dynamic changes, particularly during the onset and progression of periodontitis.

Subjects and methods

This study used Gas Chromatography-Mass Spectrometry (GC-MS), a reliable and high-throughput tool for metabolomic analysis, to detect salivary metabolic shifts across various stages of periodontitis (T1-T4). We compared differential changes in metabolites between the HC and T1 groups, the T1 and T2 groups, the T2 and T3 groups, and the T3 and T4 groups.

Results

By analysing saliva samples from 116 individuals-10 healthy controls (HC) and 106 patients with periodontitis across stages T1 (22 individuals) to T4 (28 individuals), we identified differential metabolites including Glucose, 3-Aminobutanoic Acid, N-(1-Cyclopropylethyl) Aniline, and Methylmalonic Acid. Compared to HC, the metabolites in patients with periodontitis exhibited progressive concentration changes correlating with the severity of the disease. Furthermore, KEGG pathway analysis was used to elucidate the metabolic pathways involved in the development of periodontitis.

Conclusions

Our findings demonstrate the potential of salivary metabolites as biomarkers for monitoring periodontitis progression and offer valuable insights into its pathogenesis and potential therapeutic targets.

Introduction

Periodontitis is a chronic, non-communicable disease affecting the teeth and gingiva. It is driven by various factors, including dysbiosis of oral bacterial ecology, genetic predispositions, compromised immunity, and lifestyle factors, such as smoking and diabetes [1]. Evidence indicates that periodontal therapy not only improves glycaemic control in patients with diabetes but also significantly reduces systemic inflammatory markers, affecting cytokines linked to cardiovascular diseases [2, 3]. Therefore, understanding the mechanisms underlying the development of periodontitis is of substantial importance for human health.

Metabolomics, a pivotal component of omics technologies, following genomics, transcriptomics, and proteomics, is the focus of current research. Recent advancements in metabolic research have enabled systematic evaluations of the relationship between metabolic profiles and the progression of periodontitis, leveraging high-throughput techniques to discover novel markers and potential diagnostic biomarker candidates for various diseases, including those related to amino acid and fatty acid metabolism [4,5,6,7]. Gas Chromatography-Mass Spectrometry (GC-MS) is a highly regarded metabolomics technique, particularly suited for analysing volatile and semi-volatile compounds [8], some of which are present in saliva, such as certain drugs and metabolites. Additionally, GC-MS is equipped with a rich mass spectrometry library, including mass spectrometry data of known metabolites, which improves the speed and accuracy of metabolite identification.

Saliva, as an easily accessible biological sample, is rich in metabolites and biomarkers, making it an important tool for research and diagnosis in periodontitis [9, 10]. In recent years, with the advancement of detection technology, salivary metabolomics studies have played an increasingly important role in periodontitis research [11]. Several studies have demonstrated significantly elevated levels of inflammatory factors in the saliva of periodontitis patients [12, 13]. Furthermore, alterations in enzyme activity in saliva have been strongly associated with the progression of periodontitis [14, 15]. Increases in specific metabolites also indicate a critical role for oxidative stress in the disease’s development [16]. Nevertheless, most of the present studies focus on the changes in saliva metabolism between normal patients and patients with gingivitis and periodontitis, few studies have been conducted on the metabolic changes of various stages of periodontitis.

Previous studies by our team have identified 20 differential metabolites that exhibit significant changes after periodontitis treatment [17]. This study aimed to identify specific biomarkers and evaluate the metabolite shifts corresponding to each stage of periodontitis progression. We aimed to enrich our understanding of periodontitis pathogenesis, risk assessment, and treatment strategies by charting the increase or decrease in differential metabolites and their associated metabolic pathways.

Methods

Study design and enrolled subjects

This study was approved by the Ethics Committee of Shanghai Xuhui District Dental Center (XYF AF/SC-08/01.0) and the Affiliated Hospital of Jiangsu University and adhered to the Helsinki Declaration. Written informed consent was obtained from all participants. Periodontal examination was performed at Xuhui District Dental Center by professional dentist who were previously trained and calibrated for the evaluation and sampling procedures. A cohort of 116 Chinese individuals was enrolled, and periodontitis was classified into four stages (T1-T4) based on the degree of progression [18, 19]. To estimate metabolites in saliva, we later recruited 36 Chinese individuals. According to the 2018 classification, Patients baseline characteristics and oral parameters for T1 group are: the most severe site with CAL1 to 2 mm, alveolar bone resorption up to coronal third (<15%), no tooth loss due to periodontitis. Patients for T2 group are the most severe site with CAL3 to 4 mm, alveolar bone resorption up to coronal third (15–33%), and no tooth loss due to periodontitis. Patients for T3 group are: the most severe site with CAL ≥ 5 mm, alveolar bone resorption upto1/2–2/3 of the length of the root, the number of teeth lost due to periodontitis is less than or equal to four. Patients for T4 group are the most severe site with CAL ≥ 5 mm, alveolar bone resorption upto1/2–2/3 of the length of the root, and the number of teeth lost due to periodontitis is greater than or equal to five. The baseline characteristics and oral parameters of healthy controls (HC) are: no loss of attachment, no bone resorption, and no tooth loss due to periodontitis. All participants were 18–75 years old and had no history of diabetes and smoking.

Specimen collection and Preparation

Professionally trained dentists conducted saliva collection and periodontal examinations. The study groups comprised 10 healthy controls (HC), and the patients were distributed across T1 (22 samples) and T2-T4 (28 samples each). The validation group included 12 healthy controls, T1-T2 periodontitis patients (12 samples), and T3-T4 periodontitis patients (12 samples). About 3 ml of unstimulated saliva was collected. Participants were made to sit comfortably in an upright position and tilt their heads down slightly to pool saliva in their mouths. They were made to gently spit the saliva into the graduated test tubes offered to them. Saliva samples apparently contaminated with blood were discarded and re-collected. For patients with severe periodontitis (T3-T4 group), we used the Salimetrics Transferrin kit (Salimetrics assay #1-1302) to exclude samples contaminated with blood. Ensure that all enrolled saliva samples are not contaminated with blood. The Saliva samples were then centrifuged and the supernatant thus obtained was stored at -80 °C until the assay.

For sample preparation, thawed samples were processed by adding 3 µL of saliva to a 1.5 mL Eppendorf tube containing 1 µL of L-2-chlorophenyl alanine (0.3 mg/mL in methanol) as the internal standard. After vortexing for 10 s, 1 µL of a methanol: acetonitrile mixture (2:1, vol/vol) was added. The mixture was vortexed for 1 min, sonicated for 10 min in an ice-water bath, and then stored at -20 °C for 30 min. Afterwards, the samples were centrifuged at 13,000 rpm for 10 min at 4 °C, and the supernatants were dried in a freeze-concentration centrifugal dryer. A Quality Control (QC) sample was created by pooling aliquots from all the samples. The dried supernatant was then prepared for GC-MS analysis after derivatisation with methoxylamine hydrochloride in pyridine, BSTFA (with 1% TMCS), and n-hexane.

Metabolomic profiling by GC-MS

The derivatised samples were analysed using an Agilent 7890B gas chromatography system coupled with an Agilent 5977 A MSD, and separation was achieved using a DB-5MS capillary column. Helium was used as the carrier gas and maintained at a constant flow rate of 1 mL/min. The injector was set at 260 °C, with a detailed temperature program for the oven. MS parameters included quadrupole and ion source temperatures of 150 °C and 230 °C, respectively, and a collision energy of 70 eV. Full-scan data were acquired (m/z 50–500) with a solvent delay of 1 min. The QC samples were analysed intermittently to ensure analytical repeatability.

Estimation of metabolites

Glucose estimation was performed using the GOD-POD enzymatic method (Solarbio, BC2505). Methylmalonic Acid, Glutaric Acid and Proline levels were measured by competitive enzyme-linked immunosorbent assay (ELISA) method using commercially available kit (MEIMIAN, Jiangsu, China).

Data preprocessing and statistical analysis

Raw GC-MS data in.D format were converted to.abf format for analysis in MS-DIAL, facilitating peak detection, deconvolution, and alignment. Metabolite characterisation was performed using the LUG database. A comprehensive data matrix, including sample ID, metabolite names, retention times, indices, mass-to-charge ratios, and signal intensities, was generated. Signal intensities were normalised to the internal standard, with variables exhibiting RSD > 0.3 retained for analysis. The final data matrix was subjected to Principal Component Analysis (PCA) and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) and Partial Least Squares Discriminant Analysis (PLS-DA) in R to identify differentially expressed metabolites between the groups. Cross-validation and permutation testing were used to assess model quality, with significant metabolites identified based on VIP > 1.0 and p < 0.05.

One-way ANOVA was used to compare the mean levels of salivary metabolites in three groups (healthy control group, periodontitis stage T1-2, periodontitis stage T3-4).

Results

Overview of saliva metabolite composition in periodontitis and healthy control subjects

The cohort comprised 10 HC and 106 patients with periodontitis distributed across stages T1 to T4, with 22, 28, 28, and 28 patients, respectively. Samples from each group were prepared individually, and aliquots from these samples were combined to create QC samples. The QC samples were analysed intermittently with patient samples during mass spectrometry to ensure the reliability of the analytical process. Using untargeted GC-MS analysis, 166 salivary metabolites were identified (Fig. 1A). Unsupervised PCA was used to assess the sample distribution and stability of the analytical procedure. The abundances of all detected metabolites were evaluated, followed by hierarchical clustering to categorise the metabolites (Supplementary Fig. 1).

Subsequently, we applied supervised analytical methods, PLS-DA and OPLS-DA, to pinpoint the overarching differences in metabolic profiles between the groups and identify metabolites that varied significantly across different stages of periodontitis and HC (illustrated in Fig. 1B-E and Supplementary Fig. 2). The analysis revealed clear metabolite shifts between distinct stages of periodontitis compared to HC, indicating that stage-specific metabolic alterations are associated with disease progression.

Fig. 1

Metabolomic Analysis of Periodontitis Progression. (A) Cluster heatmap of all samples; (B) The Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) score plot clearly discriminates the HC group from the T1 group. (C) The OPLS-DA score plot clearly discriminates the T2 group from the T1 group. (D) The OPLS-DA score plot clearly discriminates the T3 group from the T2 group. (E) The OPLS-DA score plot clearly discriminates the T4 group from the T3 group

Identification of key differential metabolites associated with periodontitis progression

To identify crucial metabolic markers indicative of periodontitis progression, we analysed all the detected metabolites using volcano plots. These plots incorporated a fold-change threshold (> 1.5) and Student’s t-test threshold (p < 0.05) to compare HC with each stage of periodontitis (T1-T4) (Fig. 2A-D). Our analysis identified 32 metabolites with differential expression between the T1 and HC groups (Supplementary Table S1), 17 metabolites between the T2 and T1 groups (Supplementary Table S2), 50 metabolites between the T3 and T2 groups (Supplementary Table S3), and eight metabolites between the T4 and T3 groups (Supplementary Table S4, Fig. 3E-H).

A stepwise comparison of metabolite profiles across the groups revealed that common differential metabolites were consistently associated with various stages of periodontitis (p < 0.05) (Table 1). Notably, Glucose, D-Glucose, and 3-Aminobutanoic Acid showed a gradual increase, whereas N-(1-Cyclopropylethyl) Aniline and Methylmalonic Acid decreased, comparing the HC group and the more advanced stages of periodontitis (Fig. 3A-D). Glucose and Methylmalonic Acid levels in saliva of 12 healthy people and 24 patients with periodontitis by ELISA during the validation phase. Compared with healthy people, Glucose and Methylmalonic Acid expression in patients with advanced periodontitis was higher (Fig. 2E-F). These findings underscore the potential of these metabolites as biomarkers for monitoring periodontitis progression and offer insights into the metabolic alterations that accompany the disease.

Fig. 2

Differential Metabolite Screening via Volcano Plots and salivary levels of Glucose and Methylmalonic Acid. The volcano plot can be used to visualize the p-value and Fold change value and screen the differential metabolites in groups. (A)Volcano plot of HC and T1. (B) Volcano plot of T2 and T1. (C) Volcano plot of T3 and T2. (D) Volcano plot of T4 and T3. (E) Comparison of salivary glucose (µmol/ml) between healthy controls group and periodontitis group (T1-2, T3-4). (F) Comparison of salivary Methylmalonic Acid (ng/L) between healthy controls group and periodontitis group (T1-2, T3-4). * P < 0.05, *** P < 0.001

Table 1 Increasing and decreasing metabolites from healthy controls to stages I-IV of periodontitis(n = 5)

Fig. 3

Time-Series Analysis and Heatmap Visualizations of Differential Metabolites in Periodontitis Progression. (A-D) In clusters 2 and 5 of the time-series analysis, metabolites such as D-glucose, glucose, and 3-aminobutyric acid gradually increased with the aggravation of periodontitis; metabolites such as N-(1-Cyclopropylethyl) Aniline and methylmalonic acid gradually decreased with the aggravation of periodontitis. (E) The heat map of the differential metabolites between HC and T1. (F) The heat map of the differential metabolites between T2 and T1. (G) The heat map of the differential metabolites between T3 and T2. (H) The heat map of the differential metabolites between T4 and T3

Metabolic pathway enrichment analysis

KEGG pathway enrichment analysis using differential metabolites provided insights into the metabolic pathways implicated in the development of periodontitis. The analysis yielded distinct pathway enrichments at various stages of periodontitis compared to HC and across successive periodontitis stages.

T1 vs. HC Comparison:

The pathways identified included aspects of human disease (e.g. Central carbon metabolism in cancer, Renal cell carcinoma), carbohydrate metabolism (TCA cycle), amino acid metabolism (Arginine biosynthesis, Alanine, Aspartate, and Glutamate metabolism), organismal systems (Glucagon signalling pathway), membrane transport (ABC transporters), metabolism of cofactors and vitamins, and Pyrimidine metabolism (Fig. 4A).

T2 vs. T1 Comparison:

The highlighted pathways encompassed lipid metabolism (Biosynthesis of fatty acids, Unsaturated fatty acids), carbohydrate metabolism (Glycolysis/Gluconeogenesis, Pentose phosphate pathway), and D-Amino acid metabolism (Fig. 4B).

T3 vs. T2 Comparison:

The detected pathways included human disease (Central carbon metabolism in cancer), membrane transport (ABC transporters), amino acid metabolism (D-Amino acid, Phenylalanine metabolism), carbohydrate metabolism (TCA cycle, Pyruvate metabolism, Glyoxylate and Dicarboxylate metabolism), organismal systems (Protein digestion and absorption, Glucagon signalling pathway, Mineral absorption), and Biosynthesis of unsaturated fatty acids (Fig. 4C).

T4 vs. T3 Comparison:

This comparison revealed pathways associated with human disease (Type II diabetes mellitus, Insulin resistance), carbohydrate metabolism (TCA cycle), signal transduction (HIF-1, AMPK signalling pathway), amino acid metabolism (Valine, Leucine, and Isoleucine biosynthesis), metabolism of cofactors and vitamins, and Metabolism of terpenoids and polyketides (Fig. 4D).

Fig. 4

Metabolic Pathway Enrichment Analysis of Differential Metabolites Across Periodontitis Stages. Metabolic pathway enrichment analysis of differential metabolites in each group was performed based on the KEGG database. Significant enrichment pathways were selected for bubble mapping (top 20). Pathway analysis of salivary metabolites with significant difference between H and T1 (A), between T2 and T1 (B), between T3 and T2 (C), and between T4 and T3 (D). The ordinate is the metabolic pathway name, and the abscissa is the enriched factor

Discussion

Severe periodontitis significantly affects oral health and is independently associated with systemic diseases including cardiovascular disease and type 2 diabetes [2, 3, 20]. Understanding the complex mechanisms underlying periodontitis progression and its association with systemic conditions is crucial for advancing our knowledge of its pathogenesis. This proof-of-concept study aimed to investigate the hypothesis that salivary metabolite alterations correlate with periodontitis severity, potentially illuminating disease mechanisms or aiding in diagnosis. We posited that metabolite fluctuations across periodontitis stages follow discernible patterns, with metabolomics offering insights into the end products of cellular processes and, by extension, the physiological state of an organism [21]. Utilizing GC-MS, we conducted a comparative metabolite analysis from stages T1 to T4 of periodontitis, employing statistical tools for data processing and analysis. Our objective was to identify whether common differential metabolites exhibit trends corresponding to periodontitis progression and to determine the involvement of specific metabolic pathways in disease exacerbation.

We observed that metabolites, such as Glucose, D-Glucose, 3-Aminobutanoic acid, N-(1-Cyclopropylethyl) Aniline, and Methylmalonic Acid, showed variations in salivary levels concurrent with periodontitis progression. These findings underscore the potential of saliva as a noninvasive medium for detecting metabolic changes indicative of periodontitis. Notably, alterations in carbohydrate metabolism within the salivary microbiota have been linked to periodontitis onset [22], with one study highlighting reduced carbohydrate metabolic pathway activities in subgingival plaques from patients with periodontitis [23]. This supports our findings of elevated D-Glucose and Glucose levels in severe periodontitis cases, suggesting impaired carbohydrate metabolism in the oral microbiota. N-(1-cyclopropylethyl) aniline is a biologically active compound that may have an important impact on the pathophysiological process of periodontitis [24]. Studies have shown that this compound plays a key role in cell signalling and inflammatory responses. N-(1-cyclopropylethyl) aniline analogues have been found to inhibit the release of inflammatory mediators, including tumour necrosis factor α (TNF-α) and interleukin-6 (IL-6) [25]. The excessive release of these inflammatory factors is often closely related to the pathological state of periodontitis, therefore, N-(1-cyclopropylethyl) aniline may reduce the inflammatory response of periodontal tissues and alleviate the clinical symptoms of periodontitis, such as redness, swelling, pain, and bleeding, by reducing the expression of these factors [26]. This supports our findings of reduced N-(1-cyclopropylethyl) aniline levels in severe periodontitis cases, suggesting impaired cell signalling and inflammatory responses metabolism in the oral microbiota.

Furthermore, increased blood methylmalonic acid (MMA) levels have been associated with advanced periodontitis stages [27], implicating mitochondrial dysfunction [28] and suggesting a connection between cognitive impairment and periodontitis in older individuals [27]. However, our analysis indicated a decrease in salivary MMA levels with worsening periodontitis, calling for further investigations into the metabolic functions. It is worth mentioning that the variable metabolites in distinct staging of periodontitis have not consistently been identified in our previous studies [17]. 20 abnormal metabolites were discovered in the Stage IV Grade C periodontitis which is the heaviest periodontitis. In this study these markers that differentiate between distinct periodontitis phenotypes, possibly reflect the initiation and progression of periodontitis.

Our KEGG pathway analysis linked periodontitis progression to alterations in amino acid, carbohydrate, and lipid metabolism as well as specific diseases and signalling pathways. This comprehensive metabolic overview suggests that the progression of periodontitis involves a shift in energy substrate utilisation. In conclusion, our study highlights the utility of saliva for assessing metabolic dysregulation across periodontitis stages and provides valuable biological insights into the pathogenesis of the disease and treatment options without the need for blood tests. However, the high dimensionality of metabolomics data and current limitations of metabolomics databases pose challenges in fully elucidating the metabolic networks involved in periodontitis. Future efforts should focus on dimension reduction and integrative omics analyses to better associate clinical phenotypes with periodontitis and uncover unexplored metabolic pathways.