Maik Pietzner1,2, Thomas Kocher3, Leonie Andörfer4, Birte Holtfreter4, Stefan Weiss5,6, Rutger Matthes4, Vinay Pitchika4, Carsten Oliver Schmidt7, Stefanie Samietz8, Gabi Kastenmüller9, Matthias Nauck6,1, Uwe Völker5,6, Henry Völzke6,7, Laszlo N Csonka10, Karsten Suhre9,11. 1. Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany. 2. Computational Medicine, Berlin Institute of Health (BIH), Charité-Universitätsmedizin Berlin, Berlin, Germany. 3. Department of Restorative Dentistry, Periodontology, Endodontology, and Preventive and Pediatric Dentistry, University Medicine Greifswald, Fleischmannstr. 42, 17475, Greifswald, Germany. kocher@uni-greifswald.de. 4. Department of Restorative Dentistry, Periodontology, Endodontology, and Preventive and Pediatric Dentistry, University Medicine Greifswald, Fleischmannstr. 42, 17475, Greifswald, Germany. 5. Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany. 6. DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany. 7. Institute for Community Medicine, SHIP/Clinical Epidemiology Research, University Medicine Greifswald, Greifswald, Germany. 8. Department of Prosthetic Dentistry, Gerodontology and Biomaterials, University Medicine Greifswald, Greifswald, Germany. 9. Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany. 10. Department of Biological Sciences, Purdue University, West Lafayette, USA. 11. Weill Cornell Medicine-Qatar, Education City, Qatar Foundation, Doha, Qatar.
Abstract
BACKGROUND: Periodontitis is among the most common chronic diseases worldwide, and it is one of the main reasons for tooth loss. Comprehensive profiling of the metabolite content of the saliva can enable the identification of novel pathways associated with periodontitis and highlight non-invasive markers to facilitate time and cost-effective screening efforts for the presence of periodontitis and the prediction of tooth loss. METHODS: We first investigated cross-sectional associations of 13 oral health variables with saliva levels of 562 metabolites, measured by untargeted mass spectrometry among a sub-sample (n = 938) of the Study of Health in Pomerania (SHIP-2) using linear regression models adjusting for common confounders. We took forward any candidate metabolite associated with at least two oral variables, to test for an association with a 5-year tooth loss over and above baseline oral health status using negative binomial regression models. RESULTS: We identified 84 saliva metabolites that were associated with at least one oral variable cross-sectionally, for a subset of which we observed robust replication in an independent study. Out of 34 metabolites associated with more than two oral variables, baseline saliva levels of nine metabolites were positively associated with a 5-year tooth loss. Across all analyses, the metabolites 2-pyrrolidineacetic acid and butyrylputrescine were the most consistent candidate metabolites, likely reflecting oral dysbiosis. Other candidate metabolites likely reflected tissue destruction and cell proliferation. CONCLUSIONS: Untargeted metabolic profiling of saliva replicated metabolic signatures of periodontal status and revealed novel metabolites associated with periodontitis and future tooth loss.
BACKGROUND:Periodontitis is among the most common chronic diseases worldwide, and it is one of the main reasons for tooth loss. Comprehensive profiling of the metabolite content of the saliva can enable the identification of novel pathways associated with periodontitis and highlight non-invasive markers to facilitate time and cost-effective screening efforts for the presence of periodontitis and the prediction of tooth loss. METHODS: We first investigated cross-sectional associations of 13 oral health variables with saliva levels of 562 metabolites, measured by untargeted mass spectrometry among a sub-sample (n = 938) of the Study of Health in Pomerania (SHIP-2) using linear regression models adjusting for common confounders. We took forward any candidate metabolite associated with at least two oral variables, to test for an association with a 5-year tooth loss over and above baseline oral health status using negative binomial regression models. RESULTS: We identified 84 saliva metabolites that were associated with at least one oral variable cross-sectionally, for a subset of which we observed robust replication in an independent study. Out of 34 metabolites associated with more than two oral variables, baseline saliva levels of nine metabolites were positively associated with a 5-year tooth loss. Across all analyses, the metabolites 2-pyrrolidineacetic acid and butyrylputrescine were the most consistent candidate metabolites, likely reflecting oral dysbiosis. Other candidate metabolites likely reflected tissue destruction and cell proliferation. CONCLUSIONS: Untargeted metabolic profiling of saliva replicated metabolic signatures of periodontal status and revealed novel metabolites associated with periodontitis and future tooth loss.
Authors: Richard G Watt; Blánaid Daly; Paul Allison; Lorna M D Macpherson; Renato Venturelli; Stefan Listl; Robert J Weyant; Manu R Mathur; Carol C Guarnizo-Herreño; Roger Keller Celeste; Marco A Peres; Cristin Kearns; Habib Benzian Journal: Lancet Date: 2019-07-20 Impact factor: 79.321
Authors: Marco A Peres; Lorna M D Macpherson; Robert J Weyant; Blánaid Daly; Renato Venturelli; Manu R Mathur; Stefan Listl; Roger Keller Celeste; Carol C Guarnizo-Herreño; Cristin Kearns; Habib Benzian; Paul Allison; Richard G Watt Journal: Lancet Date: 2019-07-20 Impact factor: 79.321
Authors: Menke J de Smit; Johanna Westra; Elisabeth Brouwer; Koen M J Janssen; Arjan Vissink; Arie Jan van Winkelhoff Journal: J Periodontol Date: 2015-05-13 Impact factor: 6.993