ReviewBiomaterial investigations in dentistry2026
Comparison of microleakage in restorative materials used for deep cervical margin elevation: a systematic review.
Review in Biomaterial investigations in dentistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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4 authors.
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Abstract
The present study aims to evaluate and compare the rate of microleakage (ML) in deep margin elevation (DME) of posterior teeth with subgingival margins using different materials and techniques. METHODOLOGY: An electronic search was conducted between May and August 2025 across three databases, PubMed/Medline, Scopus, and Web of Science, using the following keywords: "deep marginal elevation" OR "proximal box elevation" OR "Cervical margin relocation" OR "coronal margin relocation" OR "open sandwich technique" AND "microleakage." A secondary search was conducted in PubMed using the same terms without quotation marks. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were applied, and the search was restricted to full-text, in vitro studies published in English between 2014 and 2025 that evaluated ML in DME. Methodological quality was assessed using the Quality Assessment Tool for In Vitro Studies (QUIN) tool by two independent reviewers.
resultsA total of 53 articles were screened, and 18 were included. The most frequently compared material among the studies was resin composite, particularly bulk-fill flowable resin composite (BF-FRC) composites, while various types of glass ionomers were less commonly evaluated. Lower ML was observed with the snowplow and centripetal techniques. The type of adhesive was found to influence the marginal integrity of restorations.
conclusionThe evidence highlights the superiority of BF-FRC composites in reducing ML rates in DME when used with self-etch adhesives. Nevertheless, clinicians should remain cautious considering the limitations of the existing evidence. Regarding the technique employed, current evidence is insufficient to determine which approach more effectively reduces ML due to methodological heterogeneity and the lack of direct comparisons.
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