The present systematic review aimed to examine periodontitis-specific biomarkers in the gingival crevicular fluid (GCF) that could have a diagnostic relevance, and to provide a qualitative assessment of the current literature. Metabolites are reliable indicators of pathophysiological statuses, and their quantification in the GCF can provide an outlook of the changes associated with periodontitis and have diagnostic value. Relevant studies identified from PubMed, Embase, Cochrane Library, and Scopus databases were examined to answer the following PECO question: "In systemically healthy individuals, can concentration of specific metabolites in the GCF be used to discriminate subjects with healthy periodontium (H) or gingivitis from patients with periodontitis (P) and which is the diagnostic accuracy?" Quality of included studies was rated using a modified version of the QUADOMICS tool. Meta-analysis was conducted whenever possible. After the screening of 1,554 titles, 15 studies were selected, with sample size ranging from 30 to 93 subjects. Eleven studies performed targeted metabolomics analysis and provided data for 10 metabolites. Among the most consistent markers, malondialdehyde levels were found higher in the P group compared with H group (SMD = 2.86; 95% CI: 1.64, 4.08). Also, a significant increase of 8-hydroxy-deoxyguanosine, 4-hydroxynonenal, and neopterin was detected in periodontally diseased sites, while glutathione showed an inverse trend. When considering data from untargeted metabolomic analysis in four studies, more than 40 metabolites were found significantly discriminant, mainly related to amino acids and lipids degradation pathways. Notably, only one study reported measures of diagnostic accuracy. Several metabolites were differentially expressed in GCF of subjects across different periodontal conditions, having a major potential for investigating periodontal pathophysiology and for site-specific diagnosis. Oxidative stress-related molecules, such as malondialdehyde and 8-hydroxy-deoxyguanosine, were the most consistently associated to periodontitis (PROSPERO CRD42020188482).

Pattern analysis of the salivary metabolic profile has been proven accurate in discriminating between generalized periodontitis (GP) patients and healthy individuals (HI), as this disease modifies the salivary concentrations of specific metabolites. Due to the scarcity of data from previous studies, this study aimed to evaluate if non-surgical periodontal therapy (NST) could affect the metabolomic profile in GP patients’ saliva and if it compares to that of HI. Unstimulated salivary samples were collected from 11 HI and 12 GP patients before and 3 months after NST. Nuclear Magnetic Resonance (NMR) spectroscopy, followed by a supervised multivariate statistical approach on entire saliva spectra and partial least square (PLS) discriminant analysis, were performed to obtain metabolic profiles. In the GP group, periodontal treatment improved all clinical parameters, but not all the diseased sites were eradicated. PLS revealed an accuracy of 100% in distinguishing between metabolic profiles of GP patients before and after NST. Orthogonal projection to latent structure was able to discriminate between the three groups of subjects with an accuracy of 85.6%. However, the post-NST metabolic profile of GP patients could not be completely assimilated to that of HI. Although NST may produce significant changes in the metabolic profile, GP patients maintained a distinctive fingerprint compared to HI.

Objectives The present systematic review examined the available evidence on distinctive salivary ion profile in periodontitis compared to periodontal health and provided a qualitative assessment of the literature. Background Macro and trace elements are essential for cellular physiology, and their changes in biological fluids can be revelatory of an underlying pathological status. Methods Data from relevant studies identified from PubMed, Embase, and Scopus databases were retrieved to answer the following PECO question: “In systemically healthy individuals, are there any differences in any salivary macro or trace element concentration between periodontally healthy subjects (H) and patients with periodontitis (P)?” Quality of included studies was rated using a modified version of the QUADOMICS tool. A consistency analysis was performed to identify significantly discriminant chemical elements. Results After the screening of 873 titles, 13 studies were included reporting data on 22 different elements. Among them, levels of sodium and potassium were consistently and significantly higher in P compared to H. Conflicting results were found for all the other elements, despite concentration of calcium, copper, and manganese mostly increased in saliva of P. Levels of magnesium were found higher in P than in H in 2 studies but lower in 3. Zinc resulted significantly increased in saliva from H compared to P individuals in 2 studies, but one study reported opposite results. Four studies were considered as high quality, while reporting of operative protocols and statistical analysis was a major limitation for the others. Due to high methodologic heterogeneity, meta‐analysis was not performed. Conclusions Levels of macro or trace elements were differentially identified in saliva across diverse periodontal conditions, having a major potential for investigation of oral homeostasis and for high‐resolution periodontal diagnosis. Products of inflammatory physiologic cellular impairment, such as sodium and potassium, were the most consistently associated with periodontitis (PROSPERO CRD42021235744).

Although increasing evidence is emerging on the contribution of chemical elements in periodontal health, no studies have concomitantly evaluated the ionic profile in gingival crevicular fluid (GCF) and saliva in relation to the underlying periodontal status. Our hypothesis is that these biofluids have distinctive ionic content. Therefore, the aim of this cross-sectional study was to analyze the elemental composition of GCF and saliva in order to explore which biological matrix and which combination of elements could discriminate between periodontitis and periodontal health. Twelve ions were analyzed in GCF and unstimulated saliva from 54 subjects (18 periodontally healthy, 18 untreated severe periodontitis and 18 treated severe periodontitis) using inductively coupled plasma–mass spectrometry (ICP-MS) and inductively coupled plasma–optical emission spectroscopy (ICP-OES). These analytical techniques were able to determine levels of sodium (Na), potassium (K), calcium (Ca) and magnesium (Mg), while the other elements were below the detection threshold. Na and K ions were detected at elevated concentration in untreated periodontitis compared with treated periodontitis and healthy periodontium. Ca was increased in untreated periodontitis, but the difference was not significant. In saliva, only Na was significantly associated with periodontitis. The combination of Na and K in GCF enabled the correct assignment of a subject to the periodontitis or healthy group. Based on these preliminary results, GCF demonstrated higher clustering potential than saliva.

Pattern analysis of salivary metabolic profile has been proven accurate to discriminate generalized periodontitis (GP) patients from healthy individuals (HI) as disease modifies the salivary concentrations of specific metabolites. Due to the scarcity of data in the literature, the aim of this study was to determine whether non-surgical periodontal therapy (NST) could change salivary metabolomic profile in GP to one more similar to HI. Unstimulated whole saliva of 11 HI and 12 GP patients were obtained prior to and 3 months after NST. Metabolic profiling was performed using Nuclear Magnetic Resonance (NMR) spectroscopy, followed by supervised multivariate statistical approach on entire saliva spectra and partial least square (PLS) discriminant analysis. In GP group, periodontal treatment improved all clinical parameters, but not all the diseased sites were eradicated. PLS revealed an accuracy of 100% in discriminating the metabolomic profile of each GP patient before and after NST. OPLS was able to discriminate the 3 groups of subjects with an accuracy of 85.6%. However the post-NST metabolic profile of GP patients could not be completely assimilated to that of HS. Although NST may produce significant changes in the metabolic profile, GP patients maintained a distinctive fingerprint compared to HI.

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