Biological Variability and Biomedical Innovation in Personalized Orthodontics: A Systematic Review with Narrative Synthesis
- 1Department of General Surgery and Surgical-Medical Specialties, School of Dentistry, University of Catania, Via S. Sofia 78, 95124 Catania, Italy
- 2Department of Neurosciences, Reproductive Sciences and Oral Sciences, School of Orthodontics, University of Naples Federico II, Via Pansini 5, 80131 Naples, Italy
- 3Unit of Internationalization, Innovation and Health Research, University Hospital “G. Rodolico–San Marco”, Via Santa Sofia 78, 95123 Catania, Italy
Abstract
Background/Objectives: Orthodontic treatment response varies considerably among patients because mechanical loading interacts with individual biological, anatomical, systemic, and behavioral characteristics. Advances in mechanobiology, molecular biomarkers, omics technologies, and artificial intelligence (AI) may support a more personalized approach to treatment. This systematic review (SR) aimed to evaluate human clinical evidence concerning these domains and their potential contribution to predicting orthodontic response, improving clinical decision-making, and reducing adverse effects. Methods: A systematic search of PubMed, Scopus, and Web of Science was conducted through June 2026. Primary studies involving human participants and addressing orthodontic tooth movement (OTM), biological variability, biomarkers, genomics, epigenetics, multi-omics, or AI-supported orthodontics were selected according to predefined eligibility criteria. The review was conducted and reported in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020, and heterogeneous evidence was synthesized narratively across four predefined domains. The review protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO; registration number CRD420261486348). Risk of bias was assessed using design-appropriate instruments. Certainty of evidence was assessed at the outcome level using GRADE principles adapted to narrative synthesis. Results: The search identified 1051 records, of which 14 primary human studies were included. The clinical evidence comprised randomized trials, longitudinal and cross-sectional biomarker studies, genetic association studies, and a case report. Molecular and multi-omics studies identified candidate markers of biological response and patient phenotype. The included computational studies examined genetic association rules, gingival-enlargement classification, and AI-assisted root-resorption measurement; none demonstrated AI-guided selection of orthodontic forces. However, small samples, methodological heterogeneity, limited adjustment for confounding, and lack of external validation restricted clinical translation. Certainty of evidence supporting clinical personalization applications was predominantly very low. Conclusions: Personalized orthodontics should remain grounded in comprehensive diagnosis, biologically compatible mechanics, and individualized monitoring. Molecular and computational tools may complement clinical assessment, but routine implementation requires standardized protocols, multicenter validation, and prospective evidence of patient-relevant benefit.
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BibTeX
@article{rosa2026biological,
title = {{Biological Variability and Biomedical Innovation in Personalized Orthodontics: A Systematic Review with Narrative Synthesis}},
author = {Salvatore La Rosa and Rosalia Leonardi and Vincenzo D’Antò and Rosalia Ragusa and Gaetano Isola and Alessandro Polizzi},
journal = {Journal of Clinical Medicine},
year = {2026},
month = oct,
doi = {10.3390/jcm15207767},
url = {https://www.mdpi.com/2077-0383/15/20/7767},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Biological Variability and Biomedical Innovation in Personalized Orthodontics: A Systematic Review with Narrative Synthesis
AU - Salvatore La Rosa
AU - Rosalia Leonardi
AU - Vincenzo D’Antò
AU - Rosalia Ragusa
AU - Gaetano Isola
AU - Alessandro Polizzi
PY - 2026
DA - 2026/10/01
JO - Journal of Clinical Medicine
DO - 10.3390/jcm15207767
UR - https://www.mdpi.com/2077-0383/15/20/7767
AB - Background/Objectives: Orthodontic treatment response varies considerably among patients because mechanical loading interacts with individual biological, anatomical, systemic, and behavioral characteristics. Advances in mechanobiology, molecular biomarkers, omics technologies, and artificial intelligence (AI) may support a more personalized approach to treatment. This systematic review (SR) aimed to evaluate human clinical evidence concerning these domains and their potential contribution to predicting orthodontic response, improving clinical decision-making, and reducing adverse effects. Methods: A systematic search of PubMed, Scopus, and Web of Science was conducted through June 2026. Primary studies involving human participants and addressing orthodontic tooth movement (OTM), biological variability, biomarkers, genomics, epigenetics, multi-omics, or AI-supported orthodontics were selected according to predefined eligibility criteria. The review was conducted and reported in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020, and heterogeneous evidence was synthesized narratively across four predefined domains. The review protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO; registration number CRD420261486348). Risk of bias was assessed using design-appropriate instruments. Certainty of evidence was assessed at the outcome level using GRADE principles adapted to narrative synthesis. Results: The search identified 1051 records, of which 14 primary human studies were included. The clinical evidence comprised randomized trials, longitudinal and cross-sectional biomarker studies, genetic association studies, and a case report. Molecular and multi-omics studies identified candidate markers of biological response and patient phenotype. The included computational studies examined genetic association rules, gingival-enlargement classification, and AI-assisted root-resorption measurement; none demonstrated AI-guided selection of orthodontic forces. However, small samples, methodological heterogeneity, limited adjustment for confounding, and lack of external validation restricted clinical translation. Certainty of evidence supporting clinical personalization applications was predominantly very low. Conclusions: Personalized orthodontics should remain grounded in comprehensive diagnosis, biologically compatible mechanics, and individualized monitoring. Molecular and computational tools may complement clinical assessment, but routine implementation requires standardized protocols, multicenter validation, and prospective evidence of patient-relevant benefit.
SN - 2077-0383
ER -