The Characteristics of Microbiome and Cytokines in Healthy Implants and Peri-Implantitis of the Same Individuals
Keywords:biomarkers; cytokines; microbiota; peri-implantitis.- J Clin Med
- 4.964
- 2022 Sep 30;11(19):5817.
- Human
- Luminex
- CCL2/JE/MCP-1,CCL3/MIP-1 alpha,CCL4/MIP-1 beta,CCL5/RANTES,CCL11/Eotaxin,CCL19/MIP-3 beta,CCL20/MIP-3 alpha,CD40 Ligand/TNFSF5,CXCL1/GRO alpha/KC/CINC-1,CXCL2/GRO beta/MIP-2/CINC-3,CXCL10/IP-10/CRG-2,EGF,FGF basic/FGF2/bFGF,Flt-3 Ligand/FLT3L,G-CSF,GM-CSF,Granzyme B,IFN-alpha 2/IFNA2,IFN-beta,IFN-gamma,IL-1 alpha/IL-1F1,IL-1 beta/IL-1F2,IL-1ra/IL-1F3,IL-2,IL-3,IL-4,IL-5,IL-6,IL-7,IL-8/CXCL8,IL-9,IL-10,IL-12 p70,IL-13,IL-15,IL-17/IL-17A,IL-17E/IL-25,IL-33,Lymphotoxin-alpha/TNF-beta,PD-L1/B7-H1,PDGF-AA,PDGF-AB/BB,TGF-alpha,TNF-alpha,TRAIL/TNFSF10,VEGF
相关货号
LXLRH46-1
Abstract
Aim:To characterize the profile of submucosal microbiome and cytokine levels in peri-implant crevicular fluid (PICF) from clinically healthy implants and peri-implantitis in the same individuals. Material and methods:A total of 170 patients were screened and, finally, 14 patients with at least one healthy implant and one peri-implantitis implant were included. Submucosal microbiota and cytokines from 28 implants were analyzed using 16S rRNA gene sequencing and multifactor assays, respectively. Correlations of clinical indexes and microbiota or cytokines were analyzed using Spearman's correlation coefficient. A random forest classification model was constructed.
Results:Peri-implantitis sites harbored higher microbial diversity, as well as more Gram-negative bacteria and anaerobic bacteria, compared with healthy implants sites. The genera of Peptostreptococcaceae XIG-1, Treponema, Porphyromonas, and Lachnospiraceae G-8, as well as the cytokines of IL-17A, IL-6, IL-15, G-CSF, RANTES, and IL-1β were significantly higher in peri-implantitis than healthy implants. Furthermore, these genera and cytokines had positive relationships with clinical parameters, including probing depth (PD), bleeding on probing (BOP), and marginal bone loss (MBL). The classification model picked out the top 15 biomarkers, such as IL-17A, IL-6, IL-15, VEGF, IL-1β, Peptostreptococcaceae XIG-1, Haemophilus, and Treponema, and obtained an area under the curve (AUC) of 0.85.
Conclusions:There are more pathogenic bacteria and inflammatory cytokines in peri-implantitis sites, and biomarkers could facilitate the diagnosis of peri-implantitis.
Keywords:biomarkers; cytokines; microbiota; peri-implantitis.
Results:Peri-implantitis sites harbored higher microbial diversity, as well as more Gram-negative bacteria and anaerobic bacteria, compared with healthy implants sites. The genera of Peptostreptococcaceae XIG-1, Treponema, Porphyromonas, and Lachnospiraceae G-8, as well as the cytokines of IL-17A, IL-6, IL-15, G-CSF, RANTES, and IL-1β were significantly higher in peri-implantitis than healthy implants. Furthermore, these genera and cytokines had positive relationships with clinical parameters, including probing depth (PD), bleeding on probing (BOP), and marginal bone loss (MBL). The classification model picked out the top 15 biomarkers, such as IL-17A, IL-6, IL-15, VEGF, IL-1β, Peptostreptococcaceae XIG-1, Haemophilus, and Treponema, and obtained an area under the curve (AUC) of 0.85.
Conclusions:There are more pathogenic bacteria and inflammatory cytokines in peri-implantitis sites, and biomarkers could facilitate the diagnosis of peri-implantitis.
Keywords:biomarkers; cytokines; microbiota; peri-implantitis.
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