Giacomo Baima1, Giovanni Iaderosa2, Filippo Citterio2, Silvia Grossi2, Federica Romano2, Giovanni N Berta3, Nurcan Buduneli4, Mario Aimetti2. 1. Department of Surgical Sciences, C.I.R. Dental School, University of Turin, Turin, Italy. giacomo.baima@gmail.com. 2. Department of Surgical Sciences, C.I.R. Dental School, University of Turin, Turin, Italy. 3. Department of Clinical and Biological Sciences, University of Turin, Turin, Italy. 4. Department of Periodontology, School of Dentistry, Ege University, İzmir, Turkey.
Abstract
INTRODUCTION: Early diagnosis of periodontitis by means of a rapid, accurate and non-invasive method is highly desirable to reduce the individual and epidemiological burden of this largely prevalent disease. OBJECTIVES: The aims of the present systematic review were to examine potential salivary metabolic biomarkers and pathways associated to periodontitis, and to assess the accuracy of salivary untargeted metabolomics for the diagnosis of periodontal diseases. METHODS: Relevant studies identified from MEDLINE (PubMed), Embase and Scopus databases were systematically examined for analytical protocols, metabolic biomarkers and results from the multivariate analysis (MVA). Pathway analysis was performed using the MetaboAnalyst online software and quality assessment by means of a modified version of the QUADOMICS tool. RESULTS: Twelve studies met the inclusion criteria, with sample sizes ranging from 19 to 130 subjects. Compared to periodontally healthy individuals, valine, phenylalanine, isoleucine, tyrosine and butyrate were found upregulated in periodontitis patients in most studies; while lactate, pyruvate and N-acetyl groups were the most significantly expressed in healthy individuals. Metabolic pathways that resulted dysregulated are mainly implicated in inflammation, oxidative stress, immune activation and bacterial energetic metabolism. The findings from MVA revealed that periodontitis is characterized by a specific metabolic signature in saliva, with coefficients of determination ranging from 0.52 to 0.99. CONCLUSIONS: This systematic review summarizes candidate metabolic biomarkers and pathways related to periodontitis, which may provide opportunities for the validation of diagnostic or predictive models and the discovery of novel targets for monitoring and treating such a disease (PROSPERO CRD42020188482).
INTRODUCTION: Early diagnosis of periodontitis by means of a rapid, accurate and non-invasive method is highly desirable to reduce the individual and epidemiological burden of this largely prevalent disease. OBJECTIVES: The aims of the present systematic review were to examine potential salivary metabolic biomarkers and pathways associated to periodontitis, and to assess the accuracy of salivary untargeted metabolomics for the diagnosis of periodontal diseases. METHODS: Relevant studies identified from MEDLINE (PubMed), Embase and Scopus databases were systematically examined for analytical protocols, metabolic biomarkers and results from the multivariate analysis (MVA). Pathway analysis was performed using the MetaboAnalyst online software and quality assessment by means of a modified version of the QUADOMICS tool. RESULTS: Twelve studies met the inclusion criteria, with sample sizes ranging from 19 to 130 subjects. Compared to periodontally healthy individuals, valine, phenylalanine, isoleucine, tyrosine and butyrate were found upregulated in periodontitispatients in most studies; while lactate, pyruvate and N-acetyl groups were the most significantly expressed in healthy individuals. Metabolic pathways that resulted dysregulated are mainly implicated in inflammation, oxidative stress, immune activation and bacterial energetic metabolism. The findings from MVA revealed that periodontitis is characterized by a specific metabolic signature in saliva, with coefficients of determination ranging from 0.52 to 0.99. CONCLUSIONS: This systematic review summarizes candidate metabolic biomarkers and pathways related to periodontitis, which may provide opportunities for the validation of diagnostic or predictive models and the discovery of novel targets for monitoring and treating such a disease (PROSPERO CRD42020188482).
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