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.

Prognostic factors associated with implant loss, disease progression or favorable outcomes after peri‐implantitis surgical therapy

“…Baseline bone levels at the time of peri‐implantitis treatment emerged as the strongest prognostic factor for outcome after therapy 14–16 . Implant misplacement also played an important role in developing peri‐implantitis 17 .…”

“…Baseline bone levels at the time of peri-implantitis treatment emerged as the strongest prognostic factor for outcome after therapy. [14][15][16] Implant misplacement also played an important role in developing peri-implantitis. 17 So far, there is a lack of conclusive evidence about a possible link between systemic contribution and peri-implantitis.…”

Fusobacterium nucleatum: The Opportunistic Pathogen of Periodontal and Peri-Implant Diseases

“…Finally, how F. nucleatum interacts with the host during the evolution of peri-implant diseases, including host infection and host responses, is the most operative issue that needs to be addressed. The host immune microenvironment of peri-implant issues determines the microbial composition ( Wang et al, 2021 ). Patients with different risk levels exhibit different immune microenvironments, and F. nucleatum is distinctly detected in high-risk individuals ( Wang et al, 2021 ).…”

“…The host immune microenvironment of peri-implant issues determines the microbial composition ( Wang et al, 2021 ). Patients with different risk levels exhibit different immune microenvironments, and F. nucleatum is distinctly detected in high-risk individuals ( Wang et al, 2021 ). Host-derived marker analyses have pointed out that IL-1β is positively correlated with the probing pocket depth in experimental peri-implant models ( Monje et al, 2021 ).…”

Classical Dichotomy of Macrophages and Alternative Activation Models Proposed with Technological Progress

“…By changing the characteristics of the implant, the effect of host immune response can be regulated, and then tissue repair can be promoted [ 2 ]. Hotchkiss et al [ 36 , 37 ] have shown that macrophages are particularly important to this response, ultimately driving the conclusion of the inflammatory phase and recruiting mesenchymal stem cells (MSCs) to begin the reparative phase or recruiting other inflammatory cells to delay the healing response [ 38 , 39 ]. In fact, the polarized subtypes of macrophages have no certain advantages and disadvantages to tissue repair; for example, the formation of the vascular network can promote bone tissue regeneration, while the initiation of angiogenesis depends on M1 macrophages, while M2 macrophages play a role in promoting angiogenesis [ 40 – 42 ].…”