Dataset Information

Machine learning-assisted immune profiling stratifies peri-implantitis patients with unique microbial colonization and clinical outcomes.

ABSTRACT: Rationale: The endemic of peri-implantitis affects over 25% of dental implants. Current treatment depends on empirical patient and site-based stratifications and lacks a consistent risk grading system. Methods: We investigated a unique cohort of peri-implantitis patients undergoing regenerative therapy with comprehensive clinical, immune, and microbial profiling. We utilized a robust outlier-resistant machine learning algorithm for immune deconvolution. Results: Unsupervised clustering identified risk groups with distinct immune profiles, microbial colonization dynamics, and regenerative outcomes. Low-risk patients exhibited elevated M1/M2-like macrophage ratios and lower B-cell infiltration. The low-risk immune profile was characterized by enhanced complement signaling and higher levels of Th1 and Th17 cytokines. Fusobacterium nucleatum and Prevotella intermedia were significantly enriched in high-risk individuals. Although surgery reduced microbial burden at the peri-implant interface in all groups, only low-risk individuals exhibited suppression of keystone pathogen re-colonization. Conclusion: Peri-implant immune microenvironment shapes microbial composition and the course of regeneration. Immune signatures show untapped potential in improving the risk-grading for peri-implantitis.

SUBMITTER: Wang CW

PROVIDER: S-EPMC8171076 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Machine learning-assisted immune profiling stratifies peri-implantitis patients with unique microbial colonization and clinical outcomes.

Wang Chin-Wei CW Hao Yuning Y Di Gianfilippo Riccardo R Sugai James J Li Jiaqian J Gong Wang W Kornman Kenneth S KS Wang Hom-Lay HL Kamada Nobuhiko N Xie Yuying Y Giannobile William V WV Lei Yu Leo YL

Theranostics 20210503 14

Rationale: The endemic of peri-implantitis affects over 25% of dental implants. Current treatment depends on empirical patient and site-based stratifications and lacks a consistent risk grading system. Methods: We investigated a unique cohort of peri-implantitis patients undergoing regenerative therapy with comprehensive clinical, immune, and microbial profiling. We utilized a robust outlier-resistant machine learning algorithm for immune deconvolution. Results: Unsupervised ...[more]

PMID: 34093848

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Project description:ObjectiveTo describe the submucosal microbial profiles of peri-implantitis and healthy implants, and to explore bacteria that might be correlated with clinical parameters.MethodsIn the present cross-sectional study, 49 patients were recruited. Each patient contributed with one implant, submucosal biofilms were collected from 20 healthy implants and 29 implants with peri-implantitis. DNA was extracted and bacterial 16S ribosomal RNA (16S rRNA) genes were amplified. Submucosal biofilms were analyzed using 16S rRNA sequencing at Illumina MiSeq platform. Differences between the groups were determined by analyzing α diversity, microbial component and microbial structure. The potential correlation between the bacteria with pocket probing depth (PPD) of peri-implant calculated by Spearman correlation analysis.ResultsThe α diversity of submucosal microbial of health group was significantly lower than that in peri-implantitis group (Chao1 index: 236.85±66.13 vs. 150.54±57.43, P < 0.001; Shannon index: 3.42±0.48 vs. 3.02±0.65, P=0.032). Principal coordinated analysis showed that the submucosal microbial structure had significant difference between healthy and peri-implantitis groups [R2=0.243, P=0.001, analysis of similarities (ANOSIM)]. Compared with healthy implants, relative abundance of periodontal pathogens were higher in peri-implantitis, including members of the red complex (Porphyromonas gingivalis, Tannerella forsythia, Treponema denticola) and some members of orange complex (Precotella intermedia, Eubacterium nodatum, Parvimonas micra), as well as some new periodontal pathogens, such as Fillifactor alocis, Fretibacterium fastidiosum, Desulfobulbus sp._HMT_041, and Porphyromonas endodontalis. Spearman correlation analysis revealed that the relative abundance of Treponema denticola (r=0.686, P < 0.001), Tannerella forsythia (r=0.675, P < 0.001), Fretibacterium sp. (r=0.671, P < 0.001), Desulfobulbus sp._HMT_041 (r=0.664, P < 0.001), Filifactor alocis (r=0.642, P < 0.001), Fretibacterium fastidiosum (r=0.604, P < 0.001), Porphyromonas gingivalis (r=0.597, P < 0.001), Porphyromonas endodontalis (r=0.573, P < 0.001) were positive correlated with PPD. While the relative abundance of Rothia aeria (r=-0.615, P < 0.001) showed negatively correlation with PPD.ConclusionMarked differences were observed in the microbial profiles of healthy implants and peri-implantitis. The members of red and orange complex as well as some new periodontal pathogens seem to play an important role in peri-implant disease. Compared with healthy implants, the submucosal microbial of peri-implantitis were characterized by high species richness and diversity.

Dataset Information

Machine learning-assisted immune profiling stratifies peri-implantitis patients with unique microbial colonization and clinical outcomes.

Publications

Machine learning-assisted immune profiling stratifies peri-implantitis patients with unique microbial colonization and clinical outcomes.

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