Literature DB >> 33423574

Metabolomics Insights in Early Childhood Caries.

L H Heimisdottir1, B M Lin2, H Cho2, A Orlenko3, A A Ribeiro4, A Simon-Soro5,6,7, J Roach8, D Shungin9,10, J Ginnis1, M A Simancas-Pallares1, H D Spangler1, A G Ferreira Zandoná11, J T Wright1, P Ramamoorthy12, J H Moore3, H Koo5,6, D Wu2,13, K Divaris1,14.   

Abstract

Dental caries is characterized by a dysbiotic shift at the biofilm-tooth surface interface, yet comprehensive biochemical characterizations of the biofilm are scant. We used metabolomics to identify biochemical features of the supragingival biofilm associated with early childhood caries (ECC) prevalence and severity. The study's analytical sample comprised 289 children ages 3 to 5 (51% with ECC) who attended public preschools in North Carolina and were enrolled in a community-based cross-sectional study of early childhood oral health. Clinical examinations were conducted by calibrated examiners in community locations using International Caries Detection and Classification System (ICDAS) criteria. Supragingival plaque collected from the facial/buccal surfaces of all primary teeth in the upper-left quadrant was analyzed using ultra-performance liquid chromatography-tandem mass spectrometry. Associations between individual metabolites and 18 clinical traits (based on different ECC definitions and sets of tooth surfaces) were quantified using Brownian distance correlations (dCor) and linear regression modeling of log2-transformed values, applying a false discovery rate multiple testing correction. A tree-based pipeline optimization tool (TPOT)-machine learning process was used to identify the best-fitting ECC classification metabolite model. There were 503 named metabolites identified, including microbial, host, and exogenous biochemicals. Most significant ECC-metabolite associations were positive (i.e., upregulations/enrichments). The localized ECC case definition (ICDAS ≥1 caries experience within the surfaces from which plaque was collected) had the strongest correlation with the metabolome (dCor P = 8 × 10-3). Sixteen metabolites were significantly associated with ECC after multiple testing correction, including fucose (P = 3.0 × 10-6) and N-acetylneuraminate (p = 6.8 × 10-6) with higher ECC prevalence, as well as catechin (P = 4.7 × 10-6) and epicatechin (P = 2.9 × 10-6) with lower. Catechin, epicatechin, imidazole propionate, fucose, 9,10-DiHOME, and N-acetylneuraminate were among the top 15 metabolites in terms of ECC classification importance in the automated TPOT model. These supragingival biofilm metabolite findings provide novel insights in ECC biology and can serve as the basis for the development of measures of disease activity or risk assessment.

Entities:  

Keywords:  biofilm; children; dental caries; machine learning; microbiome; risk assessment

Mesh:

Year:  2021        PMID: 33423574      PMCID: PMC8142089          DOI: 10.1177/0022034520982963

Source DB:  PubMed          Journal:  J Dent Res        ISSN: 0022-0345            Impact factor:   8.924


  36 in total

1.  Early Childhood Caries: IAPD Bangkok Declaration.

Authors:  Nigel B Pitts; Ramon J Baez; Carolina Diaz-Guillory; Kevin J Donly; Carlos Alberto Feldens; Colman McGrath; Prathip Phantumvanit; W Kim Seow; Nikolai Sharkov; Yupin Songpaisan; Norman Tinanoff; Svante Twetman
Journal:  J Dent Child (Chic)       Date:  2019-05-15

2.  The Supragingival Biofilm in Early Childhood Caries: Clinical and Laboratory Protocols and Bioinformatics Pipelines Supporting Metagenomics, Metatranscriptomics, and Metabolomics Studies of the Oral Microbiome.

Authors:  Kimon Divaris; Dmitry Shungin; Adaris Rodríguez-Cortés; Patricia V Basta; Jeff Roach; Hunyong Cho; Di Wu; Andrea G Ferreira Zandoná; Jeannie Ginnis; Sivapriya Ramamoorthy; Jason M Kinchen; Jakub Kwintkiewicz; Natasha Butz; Apoena A Ribeiro; M Andrea Azcarate-Peril
Journal:  Methods Mol Biol       Date:  2019

3.  Measurement of Early Childhood Oral Health for Research Purposes: Dental Caries Experience and Developmental Defects of the Enamel in the Primary Dentition.

Authors:  Jeannie Ginnis; Andrea G Ferreira Zandoná; Gary D Slade; John Cantrell; Mikafui E Antonio; Bhavna T Pahel; Beau D Meyer; Poojan Shrestha; Miguel A Simancas-Pallares; Ashwini R Joshi; Kimon Divaris
Journal:  Methods Mol Biol       Date:  2019

Review 4.  Dental caries from a molecular microbiological perspective.

Authors:  B Nyvad; W Crielaard; A Mira; N Takahashi; D Beighton
Journal:  Caries Res       Date:  2012-11-30       Impact factor: 4.056

Review 5.  Tea polyphenols: application in the control of oral microorganism infectious diseases.

Authors:  Yuan Li; Xiaoge Jiang; Jianqi Hao; Yifei Zhang; Ruijie Huang
Journal:  Arch Oral Biol       Date:  2019-03-30       Impact factor: 2.633

Review 6.  Oral Microbiome Studies: Potential Diagnostic and Therapeutic Implications.

Authors:  A Mira
Journal:  Adv Dent Res       Date:  2018-02

Review 7.  Advances in the microbial etiology and pathogenesis of early childhood caries.

Authors:  E Hajishengallis; Y Parsaei; M I Klein; H Koo
Journal:  Mol Oral Microbiol       Date:  2016-02-04       Impact factor: 3.563

Review 8.  Predicting Dental Caries Outcomes in Children: A "Risky" Concept.

Authors:  K Divaris
Journal:  J Dent Res       Date:  2015-12-08       Impact factor: 6.116

9.  Model selection for metabolomics: predicting diagnosis of coronary artery disease using automated machine learning.

Authors:  Alena Orlenko; Daniel Kofink; Leo-Pekka Lyytikäinen; Kjell Nikus; Pashupati Mishra; Pekka Kuukasjärvi; Pekka J Karhunen; Mika Kähönen; Jari O Laurikka; Terho Lehtimäki; Folkert W Asselbergs; Jason H Moore
Journal:  Bioinformatics       Date:  2020-03-01       Impact factor: 6.937

10.  Missing Value Imputation Approach for Mass Spectrometry-based Metabolomics Data.

Authors:  Runmin Wei; Jingye Wang; Mingming Su; Erik Jia; Shaoqiu Chen; Tianlu Chen; Yan Ni
Journal:  Sci Rep       Date:  2018-01-12       Impact factor: 4.379

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  4 in total

Review 1.  Expert consensus on early childhood caries management.

Authors:  Jing Zou; Qin Du; Lihong Ge; Jun Wang; Xiaojing Wang; Yuqing Li; Guangtai Song; Wei Zhao; Xu Chen; Beizhan Jiang; Yufeng Mei; Yang Huang; Shuli Deng; Hongmei Zhang; Yanhong Li; Xuedong Zhou
Journal:  Int J Oral Sci       Date:  2022-07-14       Impact factor: 24.897

2.  Science for the Next Century: Deep Phenotyping.

Authors:  J T Wright; M C Herzberg
Journal:  J Dent Res       Date:  2021-03-20       Impact factor: 6.116

3.  Application of fluoride disturbs plaque microecology and promotes remineralization of enamel initial caries.

Authors:  Qianxia Zhang; Lingxia Guan; Jing Guo; Aiyun Chuan; Juan Tong; Jinghao Ban; Tian Tian; Wenkai Jiang; Shengchao Wang
Journal:  J Oral Microbiol       Date:  2022-07-27       Impact factor: 5.833

Review 4.  The promise of automated machine learning for the genetic analysis of complex traits.

Authors:  Elisabetta Manduchi; Joseph D Romano; Jason H Moore
Journal:  Hum Genet       Date:  2021-10-28       Impact factor: 5.881

  4 in total

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