Pathol. Oncol. Res. Pathology & Oncology Research Pathol. Oncol. Res. 1532-2807 Frontiers Media S.A. 1610739 10.3389/pore.2022.1610739 Pathology and Oncology Archive Original Research 20 abnormal metabolites of Stage IV Grade C periodontitis was discovered by CPSI-MS Wang et al. 10.3389/pore.2022.1610739 Wang Li-Jun 1 Liu Liu 2 Ju Wei 3 Yao Wen-Xin 1 Yang Xi-Hu 3 * Qian Wen-Hao 1 * 1 Department of Periodontitis, Xuhui District Dental Center, Shanghai, China 2 Department of Oral and Maxillofacial-Head & Neck Oncology, Ninth People's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China 3 Department of Oral and Maxillofacial Surgery, Affiliated Hospital of Jiangsu University, Zhenjiang, Jiangsu, China

Edited by: József Tímár, Semmelweis University, Hungary

 *Correspondence: Wen-Hao Qian, pingyanlaoto@163.com; Xi-Hu Yang, yangxihu1981@126.com 07 12 2022 2022 28 1610739 29 07 2022 23 11 2022 Copyright © 2022 Wang, Liu, Ju, Yao, Yang and Qian. 2022 Wang, Liu, Ju, Yao, Yang and Qian

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Saliva is a noninvasive biofluid that contains the metabolic signature of severe periodontitis (SP, Stage IV and Grade C). Conductive polymer spray ionization mass spectrometry (CPSI-MS) was used to record a wide range of metabolites within a few seconds, making this technique a promising point-of-care method for the early detection of SP (Stage IV and Grade C). Saliva samples from 31 volunteers, consisting of 16 healthy controls (HC) and 15 patients with SP (Stage IV and Grade C), were collected to identify dysregulated metabolites. Twenty metabolites were screened out, including seven amino acids. Moreover, the results showed that amino acid metabolism is closely related to the development of periodontitis. The present study further confirmed that salivary metabolites in the oral cavity were significantly altered after plaque removal. These results suggest that the combination of CPSI-MS is a feasible tool for preclinical screening of SP (Stage IV and Grade C).

 metabolomics point-of-care test conducting polymer spray ionization saliva metabolomics severe periodontitis (Stage IV and Grade C) Introduction

Periodontitis is a chronic inflammatory disease, if left untreated, that results in loss of the natural dentition and absorption of alveolar bone. It is common in adults and has a high incidence worldwide. The current understanding about the etiology of periodontitis is that this disease is caused by dysbiosis between the host response and the bacterial aggression that colonizes dental surfaces. At present, the diagnosis of periodontitis mostly depends on clinical and radiographic examinations, and then mechanical root debridement has been taken, while the specific pathogenesis of periodontitis has not yet been clarified [1].

Recently, medical fields have begun to use omics technology to add knowledge to the biomarkers of periodontitis [2–4]. Metabolomics is a branch discipline of omics that focuses on changes in metabolites and metabolic pathways to understand the pathophysiological processes in biological systems. A variety of samples are used for metabonomics studies, including saliva, blood, urine, or tissue. Saliva is a viable biofluid of oral diseases that contains many of the same biomarkers as blood and serum [5]. It is composed of a variety of locally synthesized and systemically derived molecules involved in various metabolic processes and is influenced by interactions that occur between the patient, microbiome, and environmental factors [6, 7]. Importantly, the saliva samples of a patient can be collected easily and non-invasively and have revealed specific metabolomic signatures for numerous oral and systemic diseases [8].

Consequently, metabolomics can give valuable information in different phases of periodontal diseases using spectroscopic assay techniques[9]. Nuclear magnetic resonance (NMR) and mass spectrometry (MS) were used for high-throughput identification and quantification of metabolites in biological fluids [10–12]. Several studies have identified that the metabolome of saliva samples collected from chronic periodontitis patients is different from that of healthy controls, reaching discrimination accuracies of up to 84% [13,14].

Gas chromatography mass spectrometry (GC-MS), liquid chromatography mass spectrometry (LC-MS), and nuclear magnetic resonance spectrometry (NMR) are the most widely used metabolomic methods. In recent years, ambient ionization mass spectrometry has gradually gained interest in the field of clinical diagnosis because it is free from laborious pretreatment, having a wide coverage of metabolites, with high-throughput metabolome information monitoring among various biological samples. Combined with machine learning techniques for high-dimension data interpretation, it can be performed with comparable accuracy at a lower cost [15–18].

Our previous studies have reported the practical value of conductive polymer spray ionization mass spectrometry combined with machine learning (CPSI-MS/ML) for the discrimination of oral squamous cell carcinoma (OSCC) from premalignant lesions (PML) and healthy contrast (HC) [19]. CPSI-MS/ML has shown its advantage in directly collecting hundreds of metabolite abundant data from trace dried biofluid spots within a few seconds under atmospheric conditions [20]. The characteristic metabolites previously discovered in saliva were mainly narrowed to small molecules with molecular weights of less than 500 da.

The present study aimed to determine the salivary metabolic profile of patients with chronic severe periodontitis (SP Stage IV and Grade C) using CPSI-MS, thereby providing an alternative method for SP diagnosis.

 Methods Specimens collection and preparation

All study experiments were conducted in compliance with the ethical guidelines established by the Xuhui District Dental Center. Periodontal examination was performed at Xuhui District Dental Center by professional dentist who were previously trained and calibrated for the evaluation and sampling procedures. PPD, bleeding on probing, and CALwere recorded at 6 sites per tooth. According to the 2018 classification, Stage IV and Grade C periodontitis was defined as the most severe site with CAL≥5 mm, alveolar bone resorption up to 1/2-2/3 of the length of the root, the number of teeth lost due to periodontitis is greater than or equal to five and CAL loss greater than or equal to 2 mm within 5 years. The healthy control group was those without probe bleeding, loss of attachment and bone loss. A total of 46 saliva samples were collected from 31 volunteers, including 16 HCs and 15 Stage IV and Grade C periodontitis patients (pre- and post-treatment), and all participants signed informed consent forms. Saliva was collected from 15 patients with severe periodontitis 15 min after nonsurgical periodontal therapy (NST). Saliva was prepared by centrifugation at 3,000 rpm for 5 min at room temperature. All saliva samples were stored at −80°C until use. All experimental protocols were approved by Xuhui District Dental Center.

 Metabolomic profiling by CPSI-MS

After the saliva was thawed under ambient conditions, 3 μl saliva and 1 μl 4-chloro-phenylanine (internal standard) were transferred onto the tip of a conductive polymer to form a dried saliva spot for data acquisition. Upon the addition of methanol water (7:3, v/v, 3 μl) to the dried saliva spot for metabolite extraction, a +4.5 kV high voltage was applied to the conductive polymer tip to trigger the spray ionization process and data recording using an LTQ Orbitrap Velos mass spectrometer (Thermo Scientific, San Jose, CA, United States). . The full scan range was set at m/z 50-1000 in positive mode. The data acquisition period for each case lasted 15 s to collect sufficient metabolomic data. The intensity of each metabolite ion was normalized to the average total ion current (TIC) or IS the intensity (m/z 222.0298 [M + Na]+) of each sample. Quality control (QC) samples were prepared by pooling equal volumes of 20 NC and 20 OSCC saliva samples. QC samples were analyzed throughout the run to monitor variations in the CPSI-MS system. OSCC and NC sera were alternatively arranged for the test run with the QC samples evenly inserted into the entire sequence for every 30 samples. Refer to our previous reports for specific methods and steps [19, 20].

 Metabolomics data processing

All raw files were first converted into cdf format using Xcalibur (Thermo Fisher Scientific, San Jose, CA, United States) and then imported into MATLAB 2020a (MathWorks, Natick, MA, United States) for batch data preprocessing using a self-programmed script. The metabolomic profile of each sample were obtained by averaging the mass spectra over ten continuous scans in the corresponding time window. Three of 1518 peaks were extracted to characterize the metabolomic profile. A data matrix was constructed, with each row representing one case and each column representing one peak variable (818 rows × 1518 variables). Then, the matrix goes through normalization, natural log transform, and univariate scaling before univariate analysis, multivariate analysis, and machine learning model development. Refer to our previous report for specific metabolomics data processing [20].

 Statistical analysis

Unsupervised metabolic profile differentiation between the SP and NC groups was first conducted with t-stochastic neighbor embedding (t-SNE) in MATLAB. The rank sum test was first implemented separately between the two cohorts to search for significantly different metabolite ions between the SP and NC groups. The false discovery rate (FDR) was used as the adjusted p-value to assess statistical significance. An ion was selected if its FDR value was lower than 0.05. (Orthogonal) Partial least squares discriminant analysis ({O}PLS-DA} was used for SP and HC using SIMCA-P (Umetrics, Umea, Sweden). Variables with importance in projection (VIP) higher than 1.5 were considered to contribute significantly to the pattern recognition of different SP stages. Prism (GraphPad Software, United States) was used to plot box plots, heatmaps, and receiver operating characteristic (ROC) curves.

 Results Saliva metabolic profiling of severe periodontitis patients

The collected 31 saliva cases (16 HC and 15 SP Stage IV and Grade C) were used for saliva marker discovery. A total of 301 peaks were selected to characterize the global metabolic profiles of HC and SP stages IV and C (pre- and post-treatment). The identified metabolites included amino acids, carbohydrates, lipids, and other compounds. To identify metabolic markers of SP, all metabolites were analyzed by volcano plot with fold change threshold (>1.5) and Student’s t-test threshold (p < 0.05) between severe periodontitis and HC (Figure 1A). Twenty metabolites were screened out (fold change >1.5, or <0.5, and p < 0.05), including seven amino acids (Table 1). PLS-DA and heat map analysis achieved great separation between SP and normal conditions based on the discovered metabolites (Figures 1B,C). To identify the metabolic pathways associated with the development of SP, twenty differential metabolites were performed KEGG metabolic pathway analysis (Figure 2; Table 2). The results showed that amino acid metabolism, such as tryptophan biosynthesis and phenylalanine metabolism, is closely related to the development of periodontitis.

Diagram of the saliva metabolic profiling workflow by CPSI-MS/ML. (A) Volcano graph analysis between severe periodontitis and health control. (B) Saliva metabolite PLS-DA analysis in pre-treatment, post-treatment periodontitis and healthy groups. (C) Heatmap analysis pre-treatment severe periodontitis vs. health control.

Differential metabolite between severe periodontitis (pre-treatment) and health control (n = 20).

Adduct ion Metabolite Theoretical Experimental FC p value
[M + Na]+ Taurine 148.0039 148.0037 13.38045826 2.28672E-05
[M + H]+ Pipecolic acid 130.0863 130.0861 3.949052317 0.000402786
[M + K]+ Creatinine 152.0221 152.0219 0.245158934 0.003712902
[M + H]+ leucine/isoleucine 132.1019 132.1017 8.624071109 0.005064387
[M + H]+ Phenylalanine 166.0863 166.0862 4.707537574 0.006234822
[M + Na]+ Chlorate 106.9506 106.9504 0.347728868 0.006294726
[M + K]+ Glutamine 185.0323 185.0321 9.283318609 0.008748689
[M-H2O + H]+ Tyramine 120.0814 120.0806 7.788885691 0.009620617
[M + H]+ N-Acetylcadaverine 145.1335 145.1335 4.044354976 0.016704605
[M + H]+ Putrescine 89.1073 89.10713 5.632713483 0.017023921
[M + H]+ N-Acetylputrescine 131.1179 131.1178 3.343441001 0.018178897
[M + Na]+ Citric acid 215.0162 215.0163 0.213648431 0.018644655
[M + K]+ alpha-CEHC 317.115 317.1149 1.834234529 0.019062846
[M+2K-H]+ Triethanolamine 226.0242 226.0240 9.383677694 0.028211196
[M + H-H2O]+ N1-Acetylspermine 227.2236 227.2229 3.967726161 0.028620548
[M + H]+ Arginine 175.119 175.1188 1.994356976 0.028753069
[M + H]+ Lysine 147.1128 147.1127 3.485637087 0.029056012
[M + K]+ Citrulline/Argininic acid 214.0588 214.0589 7.166101262 0.031093969
[M+2K-H]+ Methylthiophene 174.9381 174.9378 0.259340288 0.040517062
[M + K]+ Tyrosine 220.0371 220.0372 2.55777336 0.047424061

The severe periodontitis-associated metabolism pathways. Main metabolism pathways including phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism.

Pathway analysis of saliva metabolites of severe periodontitis.

Pathway Total Expected Hits P -Log10(p) Impact
Aminoacyl-tRNA biosynthesis 48 0.55742 6 9.00E-06 5.0459 0
Phenylalanine, tyrosine and tryptophan biosynthesis 4 0.046452 2 0.000754 3.1225 1
Phenylalanine metabolism 10 0.11613 2 0.005427 2.2654 0.35714
Arginine biosynthesis 14 0.16258 2 0.010676 1.9716 0.07614
Citrate cycle (TCA cycle) 20 0.23226 2 0.021391 1.6698 0.09038
Lysine degradation 25 0.29032 2 0.032636 1.4863 0
Alanine, aspartate and glutamate metabolism 28 0.32516 2 0.040286 1.3948 0.11378
Glyoxylate and dicarboxylate metabolism 32 0.37161 2 0.051436 1.2887 0.03175
D-Glutamine and D-glutamate metabolism 6 0.069677 1 0.067792 1.1688 0
Nitrogen metabolism 6 0.069677 1 0.067792 1.1688 0
Arginine and proline metabolism 38 0.44129 2 0.069981 1.155 0.08132
Tyrosine metabolism 42 0.48774 2 0.083408 1.0788 0.16435
Valine, leucine and isoleucine biosynthesis 8 0.092903 1 0.089408 1.0486 0
Taurine and hypotaurine metabolism 8 0.092903 1 0.089408 1.0486 0.42857
Ubiquinone and other terpenoid-quinone biosynthesis 9 0.10452 1 0.10004 0.99984 0
Biotin metabolism 10 0.11613 1 0.11055 0.95644 0
Glycerophospholipid metabolism 36 0.41806 1 0.34647 0.46034 0.00937
Pyrimidine metabolism 39 0.4529 1 0.36951 0.43237 0
Valine, leucine and isoleucine degradation 40 0.46452 1 0.37703 0.42363 0
Primary bile acid biosynthesis 46 0.53419 1 0.42035 0.37639 0.00758
Purine metabolism 65 0.75484 1 0.53952 0.268 0
Metabolite changes after non-surgical periodontal therapy

All 15 patients with SP (stage IV and grade C) underwent NST. Next, we evaluated whether NST could affect the metabolomic profile in the saliva of patients with SP. As shown in Table 3, twenty-four metabolites were significantly changed after NST (fold change >1.5 or <0.5 and p < 0.05, t-test) (Figure 3A). Heat map analysis also significantly distinguished between pre -and posttreatment (Figure 3B). However, there was no significant difference between the posttreatment periodontitis group and the healthy group (Figure 3C), which demonstrates the efficacy of NST. Figure 4 shows the relative intensities of the common metabolites in the three groups. These results indicated that salivary metabolites in the oral cavity were significantly altered after plaque removal.

Differential metabolite between post-treat and pre-treat group (n = 31).

Adduct ion Metabolite Theoretical (m/z) Experimental (m/z) FC p value
[M + CH3OH + H]+ 1,3-Dimethyluracil 173.0921 173.0919 0.115106013 0.033375369
[M + H]+ Glutamic acid 148.0604 148.0603 0.148412844 0.017026785
[M + K]+ Citrulline/Argininic acid 214.0588 214.0589 0.151950348 0.044749385
[M-H2O + H]+ tyramine 120.0814 120.0806 0.167856224 0.022762496
[M + Na]+ Amaranth 560.9703 560.9729 0.178158531 0.036095169
[M + K]+ N-Acetyl-glucosamine or Glucosamine 6-phosphate 260.0531 260.0533 0.206227911 0.037398582
[M + H]+ Imidazolepropionic acid or 1,3-Dimethyluracil 141.0659 141.0657 0.211537893 0.043361772
[M + H]+ Lysine 147.1128 147.1127 0.219735916 0.019980917
[M + H]+ Phenylalanine 166.0863 166.0862 0.224486539 0.012273488
[M + H]+ Putrescine 89.1073 89.10713 0.229320503 0.015428116
[M + K]+ Glutamine 185.0323 185.0321 0.237444141 0.024112654
[M + H]+ leucine/isoleucine 132.1019 132.1017 0.253025803 0.025737485
[M + H]+ N-Acetylputrescine 131.1179 131.1178 0.284009334 0.014723675
[M + H]+ Ornithine 133.0972 133.0970 0.288444073 0.03882212
[M + K]+ Homocitrulline 228.0745 228.0744 0.318278237 0.034673258
[M + K]+ Tyrosine 220.0371 220.0372 0.326325537 0.005348209
[M + H]+ Pipecolic acid 130.0863 130.0861 0.337642644 0.002353984
[M + H]+ Arginine 175.119 175.1188 0.341621155 0.002713833
[M + Na]+ Adenosine monophosphate 370.0523 370.0513 0.34623741 0.038844112
[M + H]+ N-Acetylcadaverine 145.1335 145.1335 0.380520717 0.00782618
[M + Na]+ 3-Oxoalanine 126.0162 126.0170 0.393187433 0.043574971
[M + Na]+ taurine 148.0039 148.0037 0.410119021 0.00823438
[M + K]+ Phosphocreatine 249.999 250.0032 1.802868539 0.027251416
[M+2K-H]+ Isoniazid alpha-ketoglutaric acid 341.9889 341.9881 3.041355093 0.033010147

Analysis of SP pre-treatment vs. post-treatment. (A) Volcano graph analysis between pre-treatment and post-treatment (B), heatmap analysis pre-treatment vs. post-treatment, (C) heatmap analysis of post-treatment vs. Health control.

Expression levels of differential metabolites in pre-treatment, post-treatment periodontitis and healthy groups.

 Discussion

The aim of this pilot study was to identify metabolites that differentiate severe periodontitis Stage IV and Grade C from healthy people and to reflect the changes in metabolites in patients with SP after NST using CPSI-MS.

CPSI-MS is an advanced ambient ionization MS technique, which is used for identifying the covalent structure of a metabolite and probing the variations that occur in human diseases. CPSI has become a very powerful tool for characterizing large biomolecular systems, with strong separation ability, high sensitivity, large peak capacity, excellent repeatability, and stable retention time. The key advantage of the above-described CPSI-MS procedure is that it is high-throughput and therefore well suited for big cohort analysis. Compared to conventional mass spectrometry, CPSI has no additional purification or enrichment, the sample directly loaded to form a dried biofluid spot. Both desorption and ionization can be triggered by applying an extraction solvent and a high voltage, which is as simple as operating an on/off switch. Thus, CPSI-MS is suitable for rapid, direct metabolic profiling of complex biological fluids such as serum, plasma, saliva, urine, and whole blood [20]. Moreover, direct metabolic profiling of a single case takes only a few seconds, which can collect sufficient MS data. The total analytical period for these saliva samples collected from volunteers took 0.5 h, which means that CPSI-MS can analyze approximately 100 samples within 1.2 h. In addition, the conductive polymer composite (MWCNT/PMMA) used in CPSI-MS is inexpensive, clean, and consumable, which facilitates the economic testing of large-scale samples [20].

Saliva contains a complex and variable mixture of metabolites of heterogeneous origin (e.g., host, diet, medication, and microbes) [21]. In this untargeted metabolomics study, it was possible to separate patients with SP from HCs using CPSI-MS. The present study successfully screened 20 differential metabolites in saliva samples as biomarkers to distinguish between patients with SP and HCs. The levels of glutamate, glutamine, leucine, and isoleucine in patients with SP were significantly higher than those in the HCs. Metabolomics studies of periodontitis, particularly stage IV grade C periodontitis, are limited, and our study provides potentially predictable or diagnostic metabolic markers. Moreover, KEGG metabolic pathway analysis indicated that SP may be related to the biosynthesis of aminoacyl-tRNA and arginine, lysine degradation, and amino acid metabolism (phenylalanine, alanine, aspartate, glutamate, and tyrosine). All these results suggest that there may be active amino acid metabolism in the saliva of patients with SP, and our results are consistent with a previous report [21–24].

Single metabolite fluctuations could be linked to different events occurring in health controls and disease metabolite producing changes after treatment. All 15 patients with SP received NST. Interestingly, the present study detected 24 differential metabolites after NST, and the concentration of most metabolites decreased after NST treatment, whereas the concentrations of phosphocreatine and alpha-ketoglutaric acid increased (p < 0.05). In this context, we observed that the levels of tyrosine and isoleucine decreased after NST (p < 0.005), as they reflect the host immune response to the oral microbiome and have been associated with active periodontal disease. The decreased phenylalanine level could be a consequence of reduced host tissue degradation after NST.

To the best of our knowledge, many differential metabolite variables between patients with SP and HCs have yet to be identified. The task is challenging because metabolomicss have a large and unknown complexity, and their identification may have been limited by their intrinsic properties and the single metabolomics technology platform used in this study. It can therefore be anticipated that larger sample sizes and a combination of multiple analytical platforms are required to validate the results of the present study.

 Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.

 Ethics statement

The studies involving human participants were reviewed and approved by Medical Ethics Committee of Xuhui District Dental Center, Shanghai. The patients/participants provided their written informed consent to participate in this study.

 Author contributions

Conceived and designed the experiment: L-JW, X-HY, and W-HQ. Performed the experiments: L-JW, LL, WJ, W-XY, and X-HY. Analyzed the data: L-JW, X-HY, and W-HQ. Contributed reagents/materials/analysis tools: LL, WJ, W-HQ, and X-HY. Wrote the manuscript: L-JW, X-HY, and W-HQ. All authors reviewed the manuscript.

 Funding

This study was supported by Shanghai Xuhui Medical Research Project (SHXH202114), National Natural Science Foundation of China (82173380).

 Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

 Abbreviations

CPSI-MS, Conductive polymer spray ionization mass spectrometry; CPSI-MS/ML, Conductive polymer spray ionization mass spectrometry combine with machine learning; FDR, false discovery rate; GC-MS, Gas chromatography mass spectrometry; HC, healthy control; LC-MS, liquid chromatography mass spectrometry; NMR, nuclear magnetic resonance spectrometry; NST, nonsurgical periodontal therapy; (O)PLS-DA, (Orthogonal) Partial least squares discriminant analysis; OSCC, oral squamous cell carcinoma; PML, premalignant lesion; QC, Quality control; ROC, receiver operating characteristic; SP, severe periodontitis; TIC, total ion current; t-SNE, t-stochastic neighbor embedding; VIP, Variables with importance in projection.

 References Papapanou PN Sanz M Buduneli N Dietrich T Feres M Fine DH Periodontitis: Consensus report of workgroup 2 of the 2017 world workshop on the classification of periodontal and peri-implant diseases and conditions. J Periodontol (2018) 89:S173–S182. 10.1002/JPER.17-0721 Sawle AD Kebschull M Demmer RT Papapanou PN . Identification of master regulatorgenes in human periodontitis. J Dent Res (2016) 95:1010–7. 10.1177/0022034516653588 Trindade F Oppenheim FG Helmerhorst EJ Amado F Gomes PS Vitorino R . Uncovering the molecular networks in periodontitis. Proteomics Clin Appl (2014) 8:748–61. 10.1002/prca.201400028 Grover HS Kapoor S Saksena N . Periodontal proteomics: Wonders never cease. Int J Proteomics (2013) 2013:850235. 10.1155/2013/850235 Zhang Y Sun J Lin CC Abemayor E Wang MB Wong DT . The emerging landscape of salivary diagnostics. Periodontol 2000 (2016) 70:38–52. 10.1111/prd.12099 de Almenida Pdel V Grégio AM Machado MA de Lima AA Azevedo LR . Saliva composition and functions: A comprehensive review. J Contemp Dent Pract (2008) 9:72–80. 10.5005/jcdp-9-3-72 Yoshizawa JM Schafer CA Schafer JJ Farrell JJ Paster BJ Wong DT . Salivary biomarkers: Toward future clinical and diagnostic utilities. Clin Microbiol Rev (2013) 26:781–91. 10.1128/CMR.00021-13 Wallner-Liebmann S Tenori L Mazzoleni A Dieber-Rotheneder M Konrad M Hofmann P Individual human metabolic phenotype analyzed by 1H NMR of saliva samples. J Proteome Res (2016) 15:1787–93. 10.1021/acs.jproteome.5b01060 Zhang A Sun H Yan G Wang P Wang X . Metabolomics for biomarker discovery: Moving to the clinic. Biomed Res Int (2015) 2015:354671. 10.1155/2015/354671 Vignoli A Ghini V Meoni G Licari C Takis PG Tenori L High-throughput metabolomics by 1D NMR. Angew Chem Int Ed Engl (2019) 58:968–94. 10.1002/anie.201804736 Takis PG Ghini V Tenori L Turano P Luchinat C . Uniqueness of the NMR approach to metabolomics. Trac Trends Anal Chem (2019) 120:115300. 10.1016/j.trac.2018.10.036 Nannini G Meoni G Amedei A Tenori L . Metabolomics profile in gastrointestinal cancers: Update and future perspectives. World J Gastroenterol (2020) 26:2514–32. 10.3748/wjg.v26.i20.2514 Kuboniwa M Sakanaka A Hashino E Bamba T Fukusaki E Amano A Prediction of periodontal inflammation via metabolic profiling of saliva. J Dent Res (2016) 95:1381–6. 10.1177/0022034516661142 Rzeznik M Triba MN Levy P Jungo S Botosoa E Duchemann B Identification of a discriminative metabolomic fingerprint of potential clinical relevance in saliva of patients with periodontitis using 1H nuclear magnetic resonance (NMR) spectroscopy. PLoS ONE (2017) 12:e0182767. 10.1371/journal.pone.0182767 Pu F Chiang S Zhang W Ouyang Z . Direct sampling mass spectrometry for clinical analysis. Analyst (2019) 144:1034–51. 10.1039/c8an01722k Yao YN Di D Yuan ZC Wu L Hu B . Schirmer paper noninvasive microsampling for direct mass spectrometry analysis of human tears. Anal Chem (2020) 92:6207–12. 10.1021/acs.analchem.9b05078 Mendes TPP Pereira I de Lima LAS Morais CLM Neves ACON Martin FL Lima KMG Paper spray ionization mass spectrometry as a potential tool for early diagnosis of cervical cancer. J Am Soc Mass Spectrom (2020) 31:1665–72. 10.1021/jasms.0c00111 Huang YC Chung HH Dutkiewicz EP Chen CL Hsieh HY Chen BR Predicting breast cancer by paper spray ion mobility spectrometry mass spectrometry and machine learning. Anal Chem (2020) 92:1653–7. 10.1021/acs.analchem.9b03966 Song X Narayanan R Shankar V Ethiraj S Wang X Duan N Oral squamous cell carcinoma diagnosed from saliva metabolic profiling. Proc Natl Acad Sci U S A (2020) 17:16167–73. 10.1073/pnas.2001395117 Song XW Chen H Zare RN . Conductive polymer spray ionization mass spectrometry for biofluid analysis. Anal Chem (2018) 90:12878–85. 10.1021/acs.analchem.8b03460 Kuboniwa M Sakanaka A Hashino E Bamba T Fukusaki E . Prediction of periodontal inflammation via metabolic profiling of saliva. J Dent Res (2016) 95(12):1381–6. 10.1177/0022034516661142 Barnes VM Ciancio SG Shibly O Xu T Devizio W Trivedi HM Metabolomics reveals elevated macromolecular degradation in periodontal disease. J Dent Res (2011) 90(11):1293–7. 10.1177/0022034511416240 Barnes VM Kennedy AD Panagakos F Devizio W Trivedi HM Jonsson T Global metabolomic analysis of human saliva and plasma from healthy and diabetic subjects, with and without periodontal disease. PLoS One (2014) 9(8):e105181. 10.1371/journal.pone.0105181 Aimetti M Cacciatore S Graziano A Tenori L . Metabonomic analysis of saliva reveals generalized chronic periodontitis signature. Metabolomics (2012) 8(3):465–74. 10.1007/s11306-011-0331-2