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The predictive power of saliva electrolytes exceeds that of saliva microbiomes in diagnosing early childhood caries
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Zhang, Ying; Huang, Shi; Jia, Songbo; Sun, Zheng; Li, Shanshan; Li, Fan; Zhang, Lijuan; Lu, Jie; Tan, Kaixuan; Teng, Fei; Yang, Fang
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In: J Oral Microbiol (2021)
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Abstract:
Early childhood caries (ECC) is one of the most prevalent chronic diseases affecting children worldwide, and thus its etiology, diagnosis, and prognosis are of particular clinical significance. This study aims to test the ability of salivary microbiome and electrolytes in diagnosing ECC, and their interplays within the same population. We here simultaneously profiled salivary microbiome and biochemical components of 331 children (166 caries-free (H group) and 165 caries-active children (C group)) aged 4-6 years. We identified both salivary microbial and biochemical dysbiosis associated with ECC. Remarkably, K(+), Cl(-), NH(4)(+), Na(+), SO(4)(2-), Ca(2+), Mg(2+), and Br(-) were enriched while pH and NO(3)(-) were depleted in ECC. Moreover, the dmft index (ECC severity) positively correlated with Cl(-), NH(4)(+), Ca(2+), Mg(2+), Br(-), while negatively with pH and NO(3)(-). Furthermore, machine-learning classification models were constructed based on these biomarkers from saliva microbiota, or electrolytes (and pH). Unexpectedly, the electrolyte-based classifier (AUROC = 0.94) outperformed microbiome-based (AUROC = 0.70) one and the composite-based one (with both microbial and biochemical data; AUC = 0.89) in predicting ECC. Collectively, these findings indicate ECC-associated alterations and interplays in the oral microbiota, electrolytes and pH, underscoring the necessity of developing diagnostic models with predictors from salivary electrolytes.
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Keyword:
Original Article
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URL: https://doi.org/10.1080/20002297.2021.1921486
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8131007/
http://www.ncbi.nlm.nih.gov/pubmed/34035879
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Technology roadmapping for competitive technical intelligence
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In: ISSN: 0040-1625 ; Technological Forecasting and Social Change ; https://hal-upec-upem.archives-ouvertes.fr/hal-01276909 ; Technological Forecasting and Social Change, Elsevier, 2015, pp.175-186. ⟨10.1016/j.techfore.2015.11.029⟩ (2015)
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An extended version of the fuzzy multicriteria group decision-making method in evaluation processes
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In: Faculty of Informatics - Papers (Archive) (2012)
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Development of a decision support approach for sustainable urban water management
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Theme-Based Comprehensive Evaluation in New Product Development Using Fuzzy Hierarchical Criteria Group Decision-Making Method
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In: Faculty of Engineering and Information Sciences - Papers: Part A (2011)
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Decider: A fuzzy multi-criteria group decision support system
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In: Faculty of Engineering and Information Sciences - Papers: Part A (2010)
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Multi-criteria Group Decision Support with Linguistic Variables in Long-term Scenarios for Belgian Energy Policy
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In: Faculty of Engineering and Information Sciences - Papers: Part A (2010)
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Team situation awareness measure using semantic utility functions for supporting dynamic decision-making
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In: Faculty of Engineering and Information Sciences - Papers: Part A (2010)
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