Construction of a caries diagnosis model based on microbiome novelty score

doi:10.7518/hxkq.2023.2022301

Abstract

Abstract:

Objective This study aimed to analyze the bacteria in dental caries and establish an optimized dental-ca-ries diagnosis model based on 16S ribosomal RNA (rRNA) data of oral flora. Methods We searched the public databa-ses of microbiomes including NCBI, MG-RAST, EMBL-EBI, and QIITA and collected data involved in the relevant research on human oral microbiomes worldwide. The samples in the caries dataset (1 703) were compared with healthy ones (20 540) by using the microbial search engine (MSE) to obtain the microbiome novelty score (MNS) and construct a caries diagnosis model based on this index. Nonparametric multivariate ANOVA was used to analyze and compare the impact of different host factors on the oral flora MNS, and the model was optimized by controlling related factors. Finally, the effect of the model was evaluated by receiver operating characteristic (ROC) curve analysis. Results 1) The oral microbiota distribution obviously differed among people with various oral-health statuses, and the species richness and species diversity index decreased. 2) ROC curve was used to evaluate the caries data set, and the area under ROC curve was AUC=0.67. 3) Among the five hosts’ factors including caries status, country, age, decayed missing filled tooth (DMFT) indices, and sampling site displayed the strongest effect on MNS of samples (P=0.001). 4) The AUC of the model was 0.87, 0.74, 0.74, and 0.75 in high caries, medium caries, low caries samples in Chinese children, and mixed dental plaque samples after controlling host factors, respectively. Conclusion The model based on the analysis of 16S rRNA data of oral flora had good diagnostic efficiency.

Key words: oral microbiome, big-data, caries, high-throughput sequencing, microbiome

CLC Number:

R 781.1

Sun Yanfei, Lu Jie, Yang Jiazhen, Liu Yuhan, Liu Lu, Zeng Fei, Niu Yufen, Dong Lei, Yang Fang. Construction of a caries diagnosis model based on microbiome novelty score[J]. West China Journal of Stomatology, 2023, 41(2): 208-217.

Figures/Tables 12

Fig 1

Tab 1

Fig 2

Tab 2

Tab 3

Fig 3

Fig 4

Fig 5

Tab 4

Fig 6

Fig 7

Tab 5

References 39

1	Zeng Y, Fadaak A, Alomeir N, et al. Lactobacillus plantarum disrupts S. mutans-C. albicans cross-kingdom biofilms[J]. Front Cell Infect Microbiol, 2022, 12: 872012.
2	Global Burden of Disease 2019 Cancer Collaboration, Kocarnik JM, Compton K,et al. Cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life years for 29 cancer groups from 2010 to 2019: a systematic analysis for the global burden of disease study 2019[J]. JAMA Oncol, 2022, 8(3): 420-444.
3	Kim BS, Han DH, Lee H, et al. Association of salivary microbiota with dental caries incidence with dentine involvement after 4 Years[J]. J Microbiol Biotechnol, 2018, 28(3): 454-464.
4	Wang Y, Wang S, Wu C, et al. Oral microbiome alterations associated with early childhood caries highlight the importance of carbohydrate metabolic activities[J]. m-Systems, 2019, 4(6): e00450-e00419.
5	Wu TT, Xiao J, Manning S, et al. Multimodal data integration reveals mode of delivery and snack consumption outrank salivary microbiome in association with caries outcome in Thai children[J]. Front Cell Infect Microbiol, 2022, 12: 881899.
6	Xu H, Tian BJ, Shi WH, et al. Maturation of the oral microbiota during primary teeth eruption: a longitudinal, preliminary study[J]. J Oral Microbiol, 2022, 14(1): 2051352.
7	Maruyama N, Maruyama F, Takeuchi Y, et al. Intraindividual variation in core microbiota in peri-implantitis and periodontitis[J]. Sci Rep, 2014, 4: 6602.
8	Hsiao WW, Li KL, Liu Z, et al. Microbial transformation from normal oral microbiota to acute endodontic infections[J]. BMC Genomics, 2012, 13: 345.
9	Jing G, Liu L, Wang Z, et al. Microbiome search engine 2: a platform for taxonomic and functional search of global microbiomes on the whole-microbiome level[J]. mSystems, 2021, 6(1): e00943-e00920.
10	Su X, Jing G, McDonald D, et al. Identifying and predicting novelty in microbiome studies[J]. mBio, 2018, 9(6): e02099-e02018.
11	Su X. Elucidating the beta-diversity of the microbiome: from global alignment to local alignment[J]. mSystems, 2021, 6(4): e0036321.
12	Goldberg DH, Victor JD, Gardner EP, et al. Spike train analysis toolkit: enabling wider application of information-theoretic techniques to neurophysiology[J]. Neuroinformatics, 2009, 7(3): 165-178.
13	Rognes T, Flouri T, Nichols B, et al. VSEARCH: a versatile open source tool for metagenomics[J]. PeerJ, 2016, 4: e2584.
14	Markowitz VM, Chen IMA, Palaniappan K, et al. IMG: the integrated microbial genomes database and comparative analysis system[J]. Nucleic Acids Res, 2011, 40(D1): D115-D122.
15	Jing GC, Sun Z, Wang HL, et al. Parallel-META 3: comprehensive taxonomical and functional analysis platform for efficient comparison of microbial communities[J]. Sci Rep, 2017, 7: 40371.
16	Su XQ, Xu J, Ning K. Meta-Storms: efficient search for similar microbial communities based on a novel indexing scheme and similarity score for metagenomic data[J]. Bioinformatics, 2012, 28(19): 2493-2501.
17	Joharji RM, Adenubi JO. Prevention of pit and fissure caries using an antimicrobial varnish: 9 month clinical evaluation[J]. J Dent, 2001, 29(4): 247-254.
18	Kazemtabrizi A, Haddadi A, Shavandi M, et al. Metagenomic investigation of bacteria associated with dental lesions: a cross-sectional study[J]. Med Oral Patol Oral Cir Bucal, 2020, 25(2): e240-e251.
19	Zhang L, Sun T, Zhu P, et al. Quantitative analysis of salivary oral bacteria associated with severe early childhood caries and construction of caries assessment model[J]. Sci Rep, 2020, 10(1): 6365.
20	Hurley E, Barrett MPJ, Kinirons M, et al. Comparison of the salivary and dentinal microbiome of children with severe-early childhood caries to the salivary microbiome of caries-free children[J]. BMC Oral Health, 2019, 19(1): 13.
21	Yang F, Zeng X, Ning K, et al. Saliva microbiomes distinguish caries-active from healthy human populations[J]. ISME J, 2012, 6(1): 1-10.
22	郝思远, 王甲河, 张晓奇, 等. 双歧杆菌预防龋病有效性和安全性的系统评价与Meta分析[J]. 口腔疾病防治, 2021, 29(4): 241-248.
	Hao SY, Wang JH, Zhang XQ, et al. Efficacy and safety of Bifidobacteria in preventing caries: a systematic review and meta-analysis[J]. J Prev Treat Stomatol Dis, 2021, 29(4): 241-248.
23	Human Microbiome Project Consortium. A framework for human microbiome research[J]. Nature, 2012, 486(7402): 215-221.
24	徐欣, 周学东. 龋病病因学研究与临床诊疗新进展[J]. 中华口腔医学杂志, 2021, 56(1): 3-9.
	Xu X, Zhou XD. Current progress in etiology and cli-nical management of dental caries[J]. Chin J Stomatol, 2021, 56(1): 3-9.
25	Pitts NB, Ekstrand KR, Foundation I. International Caries Detection and Assessment System (ICDAS) and its International Caries Classification and Management System (ICCMS)-methods for staging of the caries process and enabling dentists to manage caries[J]. Community Dent Oral Epidemiol, 2013, 41(1): e41-e52.
26	Simonsen RJ. From prevention to therapy: minimal intervention with sealants and resin restorative materials[J]. J Dent, 2011, 39(): S27-S33.
27	Sürme K, Kara NB, Yilmaz Y. In vitro evaluation of occlusal caries detection methods in primary and permanent teeth: a comparison of CarieScan PRO, DIAGNOdent pen, and DIAGNOcam methods[J]. Photobiomodul Photomed Laser Surg, 2020, 38(2): 105-111.
28	Bader JD, Shugars DA. A systematic review of the performance of a laser fluorescence device for detecting caries[J]. J Am Dent Assoc, 2004, 135(10): 1413-1426.
29	Teng F, Yang F, Huang S, et al. Prediction of early childhood caries via spatial-temporal variations of oral microbiota[J]. Cell Host Microbe, 2015, 18(3): 296-306.
30	Huang S, Li R, Zeng X, et al. Predictive modeling of gingivitis severity and susceptibility via oral microbiota[J]. ISME J, 2014, 8(9): 1768-1780.
31	Huang S, Li Z, He T, et al. Microbiota-based signature of gingivitis treatments: a randomized study[J]. Sci Rep, 2016, 6: 24705.
32	Duvallet C, Gibbons SM, Gurry T, et al. Meta-analysis of gut microbiome studies identifies disease-specific and shared responses[J]. Nat Commun, 2017, 8(1): 1784.
33	D’Amore R, Ijaz UZ, Schirmer M, et al. A comprehensive benchmarking study of protocols and sequencing platforms for 16S rRNA community profiling[J]. BMC Genomics, 2016, 17: 55.
34	张玉凤, 荆功超, 李劲华, 等. 基于微生物组大数据搜索的疾病检测[J]. 科学, 2021, 73(2): 24-26.
	Zhang YF, Jing GC, Li JH, et al. Disease detection based on microbiome big-data searching[J]. Science, 2021, 73(2): 24-26.
35	Su X, Jing G, Sun Z, et al. Multiple-disease detection and classification across cohorts via microbiome search[J]. mSystems, 2020, 5(2): e00150-e00120.
36	Bolyen E, Rideout JR, Dillon MR, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2[J]. Nat Biotechnol, 2019, 37(8): 852-857.
37	Ding T, Schloss PD. Dynamics and associations of microbial community types across the human body[J]. Nature, 2014, 509(7500): 357-360.
38	Simón-Soro A, Belda-Ferre P, Cabrera-Rubio R, et al. A tissue-dependent hypothesis of dental caries[J]. Caries Res, 2013, 47(6): 591-600.
39	Xu H, Tian J, Hao W, et al. Oral microbiome shifts from caries-free to caries-affected status in 3-year-old Chinese children: a longitudinal study[J]. Front Microbiol, 2018, 9: 2009.

分组	总样本数	唾液样本数	菌斑样本数
对照组	860	243	617
龋病组	843	289	554

属	基线组	对照组	龋病组
链球菌属	0.413	0.168	0.137
嗜血杆菌属	0.078	0.078	0.042

属	基线组	对照组	龋病组
普雷沃属	0.01	0.059	0.089
乳酸菌属	0.005	0.001	0.062

影响因素	F值	P值
龋病状态	57.69	0.001
DMFT指数	27.34	0.001
取样位点	56.15	0.001
年龄	56.86	0.001
国家	43.82	0.001

[1]	Yan Xinmiao, Sun Taolan, Lu Yuhang, Tan Xin, Wang Zhuo, Li Miaojing. Prediction model of dental caries in 12-year-old children in Sichuan Province based on machine learning [J]. West China Journal of Stomatology, 2023, 41(6): 686-693.
[2]	Zhang Jing, Wang Yan.. Meta-analysis of prevalence and filling rate of dental caries in preschool children in China [J]. West China Journal of Stomatology, 2023, 41(5): 573-581.
[3]	Wang Li, Wu Fei, Xiao Mo, Chen Yu-xin, Wu Ligeng. Prediction of pulp exposure risk of carious pulpitis based on deep learning [J]. West China Journal of Stomatology, 2023, 41(2): 218-224.
[4]	Zhang Qiong, Wang Jun, Xia Bin, Zhao Wei, Chen Xu, Jiang Beizhan, Huang Yang, Wu Li’an, Yuan Guohua, Zou Jing. Expert consensus on clinical management for early childhood caries [J]. West China Journal of Stomatology, 2022, 40(5): 495-503.
[5]	Li Fengjuan, Wang Liru, Xu Fengming, Wang Xu, Liu Jingjing, Wang Yanxin, Zhang Shufang.. Health status of children’s first permanent molars in Henan province from 2015 to 2020 [J]. West China Journal of Stomatology, 2022, 40(5): 560-565.
[6]	Liao Shengnan, Weitong Lü, Tang Quan, Ma Yuwen, Liu Lijia, Wang Liang, Peng Xian. Study on the inhibitory effect of selective estrogen receptor modulators on Streptococcus mutans [J]. West China Journal of Stomatology, 2022, 40(2): 218-224.
[7]	Liu Yaqi, Zhang Qiong, Wang Yan, Qu Xing, Zou Jing. Evaluation of therapeutic effect and health economics of general anesthesia and routine outpatient dental treatment in children with severe early child caries [J]. West China Journal of Stomatology, 2021, 39(6): 703-708.
[8]	Zhang Ying, Jia Songbo, Li Fan, Li Shanshan, Zhang Lijuan, Tan Kaixuan, Lu Jie, Yang Fang. Salivary biochemical indices related to early childhood caries [J]. West China Journal of Stomatology, 2021, 39(3): 300-305.
[9]	Zhang Jiali, Yao Jun, Ren Qing Cuomu, Xu Yuanhong. Analysis of dental caries and the impact factors of caries in children aged 3-5 years old in Changdu, Xizang [J]. West China Journal of Stomatology, 2021, 39(1): 53-57.
[10]	Luo Yuxue, Sun Manlin, Shi Peilei, Liu Pan, Chen Yiyin, Peng Xian. Research progress in the relationship between Veillonella and oral diseases [J]. West China Journal of Stomatology, 2020, 38(5): 576-582.
[11]	Deng Xiaoyu, Zhang Yunhan, Zou Jing, Zhang Qiong. Investigation of postoperative complications in children after dental therapy under general anesthesia [J]. West China Journal of Stomatology, 2020, 38(3): 284-289.
[12]	Qin Dan,Jiang Haofeng,Shen Lu,Zhang Cai,Chai Zhaowu,Wang Jinhua. Prevalence of dental caries and associated factors among 10-12-year-old students in Chongqing [J]. West China Journal of Stomatology, 2019, 37(6): 608-614.
[13]	Zhou Zhiyan,Xu Xin,Zhou Yuan. Research progress on carbohydrate active enzymes of human microbiome [J]. West China Journal of Stomatology, 2019, 37(6): 666-670.
[14]	Xu Titi,Zeng Liwei,Wen Jianqiong,Wan Li,Ou Xiaoyan. Prevalence of malocclusion among 5 387 12- to 14-year-old adolescents in Jiangxi province, China: an epidemiological study [J]. West China Journal of Stomatology, 2019, 37(5): 541-546.
[15]	Liu Yunkun,Xu Xin,Zhou Xuedong. Potential application of human microbiomes in the diagnosis and treatment of type 2 diabetes mellitus [J]. West China Journal of Stomatology, 2019, 37(5): 556-562.