Clinical oral implants research · 2026
Peri-Implant Crevicular Fluid Analysis by Mid-Infrared Spectroscopy in Peri-Implantitis-A Pilot Study.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Maligno Francisco, Páscoa Ricardo N M J, Gomes Pedro S
Аннотация
OBJECTIVES: This study aimed to assess biochemical differences in peri-implant crevicular fluid (PICF) using ATR-FTIR spectroscopy and multivariate analysis, through a between-participant comparison of healthy and peri-implantitis (PI) sites, and a within-participant comparison of PI-PICF and GCF from contralateral periodontal healthy teeth.
MATERIALS AND METHODS: Samples were categorized into three groups based on peri-implant status and sampling site: PICF from peri-implant healthy sites, PICF from PI-affected sites, and GCF from contralateral healthy teeth in PI patients, with n = 20/group. Samples were collected and analyzed through ATR-FTIR spectroscopy. Chemometric models were applied for cluster/outlier identification and discrimination. Regression coefficient vectors of PLS-DA models identified key spectral differences between groups.
RESULTS: A total of 40 implants were analyzed; participants had a mean age of 62 years, predominantly female (59%), non-smokers (88%), and most received regular maintenance therapy (82%). PCA revealed subtle cluster formations without clear group separation. Comparing PICF from patients with and without PI, the most important wavenumbers for the PLS-DA model were within 1700-1680 cm-1, 1190-1130 cm-1, and 1050-980 cm-1, associated with protein-related signals and nucleic-acid content. Comparing the GCF and PICF from PI patients, using a split-mouth design, the most important wavenumbers were 3006 cm-1, 2982 cm-1, and 2900 cm-1, related to lipid structures. In terms of accuracy, between-participant assessment achieved 75%, whereas the split-mouth assessment reached 83.4%.
CONCLUSIONS: FTIR-spectroscopy combined with chemometric modeling effectively discriminates peri-implant health and PI in both inter- and intra-subject comparisons. Higher within-subject accuracy supports FTIR's potential as a site-specific, non-invasive diagnostic tool for peri-implantitis.
MATERIALS AND METHODS: Samples were categorized into three groups based on peri-implant status and sampling site: PICF from peri-implant healthy sites, PICF from PI-affected sites, and GCF from contralateral healthy teeth in PI patients, with n = 20/group. Samples were collected and analyzed through ATR-FTIR spectroscopy. Chemometric models were applied for cluster/outlier identification and discrimination. Regression coefficient vectors of PLS-DA models identified key spectral differences between groups.
RESULTS: A total of 40 implants were analyzed; participants had a mean age of 62 years, predominantly female (59%), non-smokers (88%), and most received regular maintenance therapy (82%). PCA revealed subtle cluster formations without clear group separation. Comparing PICF from patients with and without PI, the most important wavenumbers for the PLS-DA model were within 1700-1680 cm-1, 1190-1130 cm-1, and 1050-980 cm-1, associated with protein-related signals and nucleic-acid content. Comparing the GCF and PICF from PI patients, using a split-mouth design, the most important wavenumbers were 3006 cm-1, 2982 cm-1, and 2900 cm-1, related to lipid structures. In terms of accuracy, between-participant assessment achieved 75%, whereas the split-mouth assessment reached 83.4%.
CONCLUSIONS: FTIR-spectroscopy combined with chemometric modeling effectively discriminates peri-implant health and PI in both inter- and intra-subject comparisons. Higher within-subject accuracy supports FTIR's potential as a site-specific, non-invasive diagnostic tool for peri-implantitis.