Intraoral scanner-based monitoring of tooth wear in young adults: 24-month results

Objectives Tooth wear causes irreversible cumulated surface loss and already occurs at a young age. Therefore, the objective of this clinical prospective observational study was to monitor the occlusal surface of a mandibular first molar in young adults for a period of 24 months. Furthermore, potential aetiological factors obtained by a questionnaire were considered. Materials and methods The study teeth (FDI #36 or #46) of 81 participants (mean age 22.8 +/- 2.2 years) were scanned with the intraoral scanner (IOS, Trios 3, 3Shape) at the second follow-up (T2) after an observation period of 24 months. Standard-tessellation-language datasets were superimposed with baseline (T0) and T2 scans in 3D analysis software (GOM Inspect). The maximum vertical substance loss was measured between T0 and T2 at 6/7 areas (4/5 cusps and 2 ridges) of each study tooth and data compared to the already published data of the first follow-up (T1) after 12-month observation period. The morphology of tooth wear was classified into three groups: cupping (C), facet (F) and combined cupping-facet (CF). The analysis of aetiological factors, such as acid impacts, was based on a questionnaire filled out by participants at time points T0, T1 and T2. Non-parametric tests were used for statistical analysis (p < 0.05). Results The buccal load-bearing cusps (mesiobuccal: median 15 mu m, 95%CI 11/18 mu m; mesiolingual 8 mu m, 0/11 mu m) were most affected by tooth wear. Loss values increased significantly at T2 compared to T1 for all areas, although significantly less than in the first 12 months (T0-T1). Areas that already exhibited F at T0 mostly displayed them also at T2 and only rarely developed further into C or CF. The only association between aetiological factors and loss values could be detected for sex as males had significantly higher loss values than females. Conclusions Progression of tooth wear could be clearly shown with high interindividual variations in loss values among participants. This indicates the need for individual monitoring with IOS.