Методи багатовимірного статистичного аналізу в дослідженнях мікробіому порожнини рота при захворюваннях пародонта та післяімплантаційних ускладненнях: сучасний стан і перспективи застосування

Vitalii Bida; Vladyslav Humeniuk

doi:10.33295/1992-576X-2026-2-ORSR-4

Authors

Vitalii Bida Shupyk National University of Health of Ukraine
Vladyslav Humeniuk Shupyk National University of Health of Ukraine

DOI:

https://doi.org/10.33295/1992-576X-2026-2-ORSR-4

Keywords:

antimicrobial resistance, heatmap, hierarchical clustering, multivariate statistical analysis, oral microbiome, peri-implantitis, personalised dentistry, principal component analysis, ROC analysis

Abstract

Background. The oral microbiome of patients requiring dental implantation, particularly those with periodontal disease, is characterised by dozens of interrelated variables that form a highly complex system. Conventional univariate statistical methods actively used in dental research fail to capture this structural complexity and to reveal hidden patterns that are critical for personalising clinical decisions.

Objective. To analyse the current state of application of multivariate statistical methods (heatmaps, hierarchical clustering, principal component analysis (PCA), and ROC analysis) in oral microbiome research on periodontal diseases and peri-implant complications, to substantiate their advantages over traditional approaches, and to outline prospects for implementation in scientific research and clinical practice.

Materials and methods. A comprehensive analysis of 48 peer-reviewed publications from the PubMed, Scopus, and Web of Science databases (2015–2025) was performed. Additionally, the study incorporated clinical and microbiological data from a pilot cohort to evaluate the practical implementation of unsupervised clustering and heatmap visualization.

Results. Hierarchical clustering allows for the identification of clinically significant patient subgroups based on microbial profiles that correlate with PPD and CAL, which go undetected by conventional analysis. PCA effectively reduces the dimensionality of complex microbiological matrices and uncovers latent axes of variability associated with dysbiosis. Heatmaps combined with clustering enable simultaneous visualisation of individual microbial profiles and AMR patterns—a level of integration that remains unachievable with standard microbiological approaches.

Conclusions. Multivariate analysis methods are significantly more effective than traditional univariate approaches for studying the oral microbiome in periodontal diseases and peri-implant complications, holding considerable potential for implementation in personalised dental practice.

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