Supragingival Biomarker flora of Children With and Without Cariogenic Disease and Black Stains, Aged 3 to 6 Years

Q: What is the study’s goal for children aged 3 to 6?

To identify keystone microbiota and potential biomarkers related to dental caries and black stain pigment in children aged 3 to 6 years.

Q: How were supragingival plaques collected and analyzed?

Supragingival plaques were collected from 122 children and processed for 16S rRNA sequencing, followed by bioinformatics analyses including diversity, differential taxa, and machine learning evaluation.

Q: Do overall microbial diversity levels differ between healthy and disease groups?

Alpha diversity results showed similar richness and diversity across healthy controls, BS-only, SECC-only, and SECC+BS groups (P > .05).

Sep 30, 2026

Upload by Genevieve

Background: The oral microbiome plays a pivotal role in the occurrence and progression of dental caries and black stain (BS) pigment. Objectives: Explore the keystone microbiota and potential biomarkers of caries and BS pigment in 3 to 6-year-old children. Methods: 122 children (HC, SECC, BSCF, SECCBS) provided supragingival plaque for 16S rRNA sequencing and bioinformatics analysis. Results: Community richness and diversity were similar, while specific taxa and machine-learning models distinguished groups. Conclusions: Diversity has limited influence, but candidate bacteria may predict caries and BS via network and differential learning.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages21

Reading Time53 min

AccessFree to download

Introduction

Background

Objectives

Methods

Results

Diversity and keystone nodes

Machine learning model evaluation

Conclusions

Scientiﬁc Research Report Supragingival Biomarker ﬂora of Children With and Without Cariogenic Disease and Black Stains, Aged 3 to 6 Years Li Zhang a,b,y, Aobo Dua,c,y, Ying Chen b, Dali Zheng d, Youguang Lua,d* a Department of Preventive Dentistry, School and Hospital of Stomatology, Fujian Medical University, Fuzhou, China b Department of Stomatology, Shenzhen Children’s Hospital, Shenzhen, Guangdong, China c Yiwu Stomatological Hospital, Yiwu, China d Fujian Key Laboratory of Oral Diseases, Fujian Provincial Biological Materials Engineering and Technology Centre of Stomatology, Fuzhou, China A R T I C L E I N F O Article history: Received 21 August 2025 Received in revised form 28 September 2025 Accepted 1 October 2025 Available online 18 December 2025 Key words: Oral microbiota 16S rRNA Meta-correlation Keystone Machine learning A B S T R A C T Background: The oral microbiome plays a pivotal role in the occurrence and progression of dental caries and black stain (BS) pigment. Objectives: The aim of this study was to explore the keystone microbiota and potential biomarkers ofcaries and BS pigment in 3 to 6-year-old children. Methods: A total of 122 children were included, namely, healthy controls (HC, n = 32), those with severe early childhood caries (SECC, n = 31), those with BS pigment but caries-free (BSCF, n = 29), and those with SECC and BS pigment (SECCBS, n = 30) . Supragingival plaques were collected for 16S rRNA sequencing followed by bioinformatics analysis. Results: Seven phyla and 14 genera were identiﬁed in all the samples, and differences in relative abundance were observed. Alpha diversity analysis revealed that the richness and diversity of the bacterial communities were similar across the HC, BSCF, SECC and SECCBS groups (P > .05). Different bacterial species were identiﬁed in the six paired groups (P < .05) . With respect to the disparities in keystone nodes, the SECC group had the highest value of 66, followed by the SECCBS and BSCF groups and the HC group (56, 47 and 33, respectively) . The areas under roc curve for the 10 machine learning models were systematically evaluated, and seven models yielded exceptional results, including support vector machine (SVM)-linear and SVM-RBF for BSCF−SECC, nay¨ve Bayes classiﬁcation for BSCF−SECCBS, decision trees for HC−BSCF, LASSO for HC−SECC, and SVM-poly for HC−SECCBS and K nearest neighbour for SECC−SECCBS. Conclusions: The diversity of the microbial community has little inﬂuence on the development of dental caries and black staining. However, speciﬁc bacteria exhibited different relative abundances across the HC, SECC, BSCF, and SECCBS groups; therefore, those bacteria may serve as candidate biomarkers. Co-occurrence network approaches and differential machine learning models can be used to predict a spectrum of dental caries in primary dentition, providing a convenient and preventive strategy. 􀀁 2025 The Authors. Published by Elsevier Inc. on behalf ofFDI World Dental Federation. This is an open access article under the CC BY license ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)) * Corresponding author. Department of Preventive Dentistry, School and Hospital of Stomatology, Fujian Medical University, 246 Middle Yangqiao Road, Fuzhou 350002, China. E-mail address: [fjlyg63@fjmu.edu.cn](fjlyg63@fjmu.edu.cn) (Y. Lu). Li Zhang: [http://orcid.org/0009-0009-8023-6040](http://orcid.org/0009-0009-8023-6040) [y](y Li Zhang and Aobo Du contributed equally to this study.)[ Li Zhang and Aobo Du contributed equally to this study.](y Li Zhang and Aobo Du contributed equally to this study.) [https://doi.org/10.1016/j.identj.2025.103982](https://doi.org/10.1016/j.identj.2025.103982) Introduction Dental decay, a public health issue, negatively affects the quality of life of children and their families.1 When left unaddressed in young patients,2 oral diseases can lead to multip

FAQs

What is the study’s goal for children aged 3 to 6?

To identify keystone microbiota and potential biomarkers related to dental caries and black stain pigment in children aged 3 to 6 years.

How were supragingival plaques collected and analyzed?

Supragingival plaques were collected from 122 children and processed for 16S rRNA sequencing, followed by bioinformatics analyses including diversity, differential taxa, and machine learning evaluation.

Do overall microbial diversity levels differ between healthy and disease groups?

Alpha diversity results showed similar richness and diversity across healthy controls, BS-only, SECC-only, and SECC+BS groups (P > .05).

How can the findings help prediction or prevention?

Specific bacteria showed different relative abundances across groups, and co-occurrence networks combined with differential machine-learning models can be used to predict dental caries spectrum in primary dentition, supporting a convenient preventive strategy.

Sep 30, 2026

Upload by Genevieve