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

Chin Wei Wang, Yuning Hao, Riccardo Di Gianfilippo, James Sugai, Jiaqian Li, Wang Gong, Kenneth S. Kornman, Hom Lay Wang, Nobuhiko Kamada, Yuying Xie, William V. Giannobile, Yu Leo Lei

研究成果: 雜誌貢獻文章同行評審

6 引文 斯高帕斯(Scopus)

摘要

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.
原文英語
頁(從 - 到)6703-6716
頁數14
期刊Theranostics
11
發行號14
DOIs
出版狀態已發佈 - 2021
對外發佈

ASJC Scopus subject areas

  • 醫藥(雜項)
  • 藥理學、毒理學和藥劑學(雜項)

指紋

深入研究「Machine learning-Assisted immune profiling stratifies peri-implantitis patients with unique microbial colonization and clinical outcomes」主題。共同形成了獨特的指紋。

引用此