{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T00:03:10Z","timestamp":1793318590781,"version":"4.5.0"},"reference-count":32,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T00:00:00Z","timestamp":1748995200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100011970","name":"International Team for Implantology","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100011970","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000781","name":"European Research Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000781","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Clinic Periodontology"],"published-print":{"date-parts":[[2025,7]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:sec><jats:title>Aim<\/jats:title><jats:p>This observational study aimed to verify and improve the predictive value of plaque microbiome of patients with dental implant for peri\u2010implant diseases.<\/jats:p><\/jats:sec><jats:sec><jats:title>Materials and Methods<\/jats:title><jats:p>Patients were included in one of the following study groups according to the health status of their dental implants: (a) healthy, (b) affected by mucositis and (c) affected by peri\u2010implantitis. From each patient, submucosal plaque microbiome samples were collected from the considered dental implant and from a contralateral healthy implant\/tooth. After shotgun metagenomic sequencing, the plaque microbiome was profiled taxonomically and functionally with MetaPhlAn 4 and HUMAnN 3, respectively. Taxonomic and functional profiles were fed into machine\u2010learning models, which were then evaluated with cross\u2010validation to assess the extent to which the plaque microbiome could be used to pinpoint peri\u2010implant diseases.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Shotgun metagenomics sequencing was performed for a total of 158 samples spanning 102 individuals. Four\u2010hundred and forty\u2010seven prokaryotic species were identified as part of the peri\u2010implant microbiome, 34% of which were currently uncharacterized species. At the community level, the peri\u2010implant microbiome differed according to the health status of the implant (<jats:italic>p<\/jats:italic>\u2009\u2264\u20090.006 for all pairwise comparisons) but this was site\u2010specific, as healthy contralateral sites showed no discriminating microbiome features. Peri\u2010implantitis microbiomes further showed lower inter\u2010subject variability than healthy plaque microbiomes (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.001), while mucositis\u2010associated microbiomes were in the middle of the continuum between health and peri\u2010implantitis. Each health condition was associated with a strong signature of taxonomic and functional microbiome biomarkers (log<jats:sub>10<\/jats:sub> LDA score \u2265\u20092.5), 30% and 13% of which represented uncharacterized microbial functions and unknown species, respectively. Distinct <jats:styled-content style=\"fixed-case\">\n<jats:italic>Fusobacterium nucleatum<\/jats:italic>\n<\/jats:styled-content> clades were associated with implant status, highlighting the subspecies <jats:styled-content style=\"fixed-case\">\n<jats:italic>F. nucleatum<\/jats:italic>\u2032s<\/jats:styled-content> functional and phenotypic diversity. Machine\u2010learning models trained on taxonomic or functional plaque microbiome profiles were highly accurate in differentiating clinical groups (AUC\u2009=\u20090.78\u20130.96) and highlighted the extent to which the peri\u2010implant microbiome is associated with peri\u2010implant clinical parameters (AUC\u2009=\u20090.79\u20130.87).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Plaque microbiome profiling with shotgun metagenomics revealed consistent associations between microbiome composition and peri\u2010implant diseases. In addition to pointing to peri\u2010implant\u2010associated microbes, warranting further mechanistic studies, we showed high\u2010resolution plaque microbiome evaluation via metagenomics as an effective tool. Its utility within protocols for clinical management of peri\u2010implant diseases should be explored in the future.<\/jats:p><\/jats:sec>","DOI":"10.1111\/jcpe.14121","type":"journal-article","created":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T20:50:34Z","timestamp":1749070234000},"page":"999-1010","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Shotgun Metagenomics Identifies in a Cross\u2010Sectional Setting Improved Plaque Microbiome Biomarkers for Peri\u2010Implant Diseases"],"prefix":"10.1111","volume":"52","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8444-0489","authenticated-orcid":false,"given":"Paolo","family":"Ghensi","sequence":"first","affiliation":[{"name":"PreBiomics S.r.l.  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