AI Article Synopsis

  • Dental implants are becoming more popular, but conditions like mucositis and peri-implantitis pose significant health and economic challenges due to microbial biofilms affecting the tissues around implants.
  • A study using metagenomic sequencing of 113 samples from 72 individuals identified specific bacteria linked to these diseases, creating a "peri-implantitis-related complex" (PiRC) with seven key bacteria.
  • The research found that the microbiome varies by site, with healthy areas resembling healthy implants, and highlighted Fusobacterium nucleatum as a crucial player in mucositis, demonstrating the potential of microbiome-based machine learning for diagnosing and understanding these conditions.

Article Abstract

Dental implants are installed in an increasing number of patients. Mucositis and peri-implantitis are common microbial-biofilm-associated diseases affecting the tissues that surround the dental implant and are a major medical and socioeconomic burden. By metagenomic sequencing of the plaque microbiome in different peri-implant health and disease conditions (113 samples from 72 individuals), we found microbial signatures for peri-implantitis and mucositis and defined the peri-implantitis-related complex (PiRC) composed by the 7 most discriminative bacteria. The peri-implantitis microbiome is site specific as contralateral healthy sites resembled more the microbiome of healthy implants, while mucositis was specifically enriched for Fusobacterium nucleatum acting as a keystone colonizer. Microbiome-based machine learning showed high diagnostic and prognostic power for peri-implant diseases and strain-level profiling identified a previously uncharacterized subspecies of F. nucleatum to be particularly associated with disease. Altogether, we associated the plaque microbiome with peri-implant diseases and identified microbial signatures of disease severity.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7603341PMC
http://dx.doi.org/10.1038/s41522-020-00155-7DOI Listing

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