The salivary microbiome as a diagnostic biomarker of periodontitis: a 16S multi-batch study before and after the removal of batch effects.

Autor:	Regueira-Iglesias A; Oral Sciences Research Group, Special Needs Unit, Department of Surgery and Medical-Surgical Specialties, School of Medicine and Dentistry, Instituto de Investigación Sanitaria de Santiago (IDIS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain., Suárez-Rodríguez B; Oral Sciences Research Group, Special Needs Unit, Department of Surgery and Medical-Surgical Specialties, School of Medicine and Dentistry, Instituto de Investigación Sanitaria de Santiago (IDIS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain., Blanco-Pintos T; Oral Sciences Research Group, Special Needs Unit, Department of Surgery and Medical-Surgical Specialties, School of Medicine and Dentistry, Instituto de Investigación Sanitaria de Santiago (IDIS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain., Relvas M; Instituto Universitário de Ciências da Saúde, Cooperativa de Ensino Superior Politécnico e Universitário (IUCS-CESPU), Unidade de Investigação em Patologia e Reabilitação Oral (UNIPRO), Gandra, Portugal., Alonso-Sampedro M; Department of Internal Medicine and Clinical Epidemiology, Instituto de Investigación Sanitaria de Santiago (IDIS), Complejo Hospitalario Universitario, Santiago de Compostela, Spain., Balsa-Castro C; Oral Sciences Research Group, Special Needs Unit, Department of Surgery and Medical-Surgical Specialties, School of Medicine and Dentistry, Instituto de Investigación Sanitaria de Santiago (IDIS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain., Tomás I; Oral Sciences Research Group, Special Needs Unit, Department of Surgery and Medical-Surgical Specialties, School of Medicine and Dentistry, Instituto de Investigación Sanitaria de Santiago (IDIS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain.
Jazyk:	angličtina
Zdroj:	Frontiers in cellular and infection microbiology [Front Cell Infect Microbiol] 2024 Jul 12; Vol. 14, pp. 1405699. Date of Electronic Publication: 2024 Jul 12 (Print Publication: 2024).
DOI:	10.3389/fcimb.2024.1405699
Abstrakt:	Introduction: Microbiome-based clinical applications that improve diagnosis related to oral health are of great interest to precision dentistry. Predictive studies on the salivary microbiome are scarce and of low methodological quality (low sample sizes, lack of biological heterogeneity, and absence of a validation process). None of them evaluates the impact of confounding factors as batch effects (BEs). This is the first 16S multi-batch study to analyze the salivary microbiome at the amplicon sequence variant (ASV) level in terms of differential abundance and machine learning models. This is done in periodontally healthy and periodontitis patients before and after removing BEs. Methods: Saliva was collected from 124 patients (50 healthy, 74 periodontitis) in our setting. Sequencing of the V3-V4 16S rRNA gene region was performed in Illumina MiSeq. In parallel, searches were conducted on four databases to identify previous Illumina V3-V4 sequencing studies on the salivary microbiome. Investigations that met predefined criteria were included in the analysis, and the own and external sequences were processed using the same bioinformatics protocol. The statistical analysis was performed in the R-Bioconductor environment. Results: The elimination of BEs reduced the number of ASVs with differential abundance between the groups by approximately one-third (Before=265; After=190). Before removing BEs, the model constructed using all study samples (796) comprised 16 ASVs (0.16%) and had an area under the curve (AUC) of 0.944, sensitivity of 90.73%, and specificity of 87.16%. The model built using two-thirds of the specimens (training=531) comprised 35 ASVs (0.36%) and had an AUC of 0.955, sensitivity of 86.54%, and specificity of 90.06% after being validated in the remaining one-third (test=265). After removing BEs, the models required more ASVs (all samples=200-2.03%; training=100-1.01%) to obtain slightly lower AUC (all=0.935; test=0.947), lower sensitivity (all=81.79%; test=78.85%), and similar specificity (all=91.51%; test=90.68%). Conclusions: The removal of BEs controls false positive ASVs in the differential abundance analysis. However, their elimination implies a significantly larger number of predictor taxa to achieve optimal performance, creating less robust classifiers. As all the provided models can accurately discriminate health from periodontitis, implying good/excellent sensitivities/specificities, the salivary microbiome demonstrates potential clinical applicability as a precision diagnostic tool for periodontitis. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2024 Regueira-Iglesias, Suárez-Rodríguez, Blanco-Pintos, Relvas, Alonso-Sampedro, Balsa-Castro and Tomás.)
Databáze:	MEDLINE
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