Journal of clinical periodontology · 2025
Microbial Markers for Diagnosis and Risk Assessment for Periodontal Diseases: A Systematic Literature Search and Narrative Synthesis.
СтатьяСистематический обзорПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Bostanci Nagihan, Manoil Daniel, Van Holm Wannes, Belibasakis Georgios N, Teughels Wim
Аннотация
AIM: To examine the accuracy of microbiological biomarkers in diagnosing periodontal diseases, specifically addressing three focus questions: (FQ1) distinguishing health from disease; (FQ2) predicting disease progression; and (FQ3) assessing treatment outcomes.
MATERIALS AND METHODS: A PRISMA-guided search in MEDLINE, EMBAS and WEB OF SCIENCE included cross-sectional and longitudinal studies (e.g., randomised controlled trials, cohort studies) with ≥ 20 participants per group. Eligible studies involved individuals diagnosed with periodontal health, gingivitis or periodontitis, based on well-defined clinical criteria, and utilised microbiological analyses of oral fluids and/or dental plaque. Diagnostic accuracy had to be evaluated using sensitivity, specificity or area under the receiver operating characteristic (ROC) curve (AUC), or alternatively, data for their computation had to be provided.
RESULTS: Thirty-one studies were included, mostly cross-sectional or case-control, with significant variability in sampling sites, microbial analyses and diagnostic definitions, complicating direct comparisons. Frequently investigated biomarkers included Aggregatibacter actinomycetemcomitans (JP2 genotype), Porphyromonas gingivalis , Tannerella forsythia and Treponema denticola . The highest diagnostic accuracy (AUC > 0.95) was achieved through composite microbiome-based metrics such as the subgingival microbial dysbiosis index. However, methodological heterogeneity and inconsistent criteria limited reliability.
CONCLUSION: Although microbiological biomarkers hold promise for periodontal disease diagnostics and monitoring, current evidence is insufficient for clinical implementation. Future research should standardise methodologies, sampling protocols and diagnostic criteria to ensure robust validation and facilitate integration into precision dentistry.
MATERIALS AND METHODS: A PRISMA-guided search in MEDLINE, EMBAS and WEB OF SCIENCE included cross-sectional and longitudinal studies (e.g., randomised controlled trials, cohort studies) with ≥ 20 participants per group. Eligible studies involved individuals diagnosed with periodontal health, gingivitis or periodontitis, based on well-defined clinical criteria, and utilised microbiological analyses of oral fluids and/or dental plaque. Diagnostic accuracy had to be evaluated using sensitivity, specificity or area under the receiver operating characteristic (ROC) curve (AUC), or alternatively, data for their computation had to be provided.
RESULTS: Thirty-one studies were included, mostly cross-sectional or case-control, with significant variability in sampling sites, microbial analyses and diagnostic definitions, complicating direct comparisons. Frequently investigated biomarkers included Aggregatibacter actinomycetemcomitans (JP2 genotype), Porphyromonas gingivalis , Tannerella forsythia and Treponema denticola . The highest diagnostic accuracy (AUC > 0.95) was achieved through composite microbiome-based metrics such as the subgingival microbial dysbiosis index. However, methodological heterogeneity and inconsistent criteria limited reliability.
CONCLUSION: Although microbiological biomarkers hold promise for periodontal disease diagnostics and monitoring, current evidence is insufficient for clinical implementation. Future research should standardise methodologies, sampling protocols and diagnostic criteria to ensure robust validation and facilitate integration into precision dentistry.