Wedad Hammoudi1, Mats Trulsson2, Jan-Ivan Smedberg3, Peter Svensson4. 1. Dept. of Prosthetic Dentistry, Folktandvården Eastmaninstitutet, Stockholm, Sweden; Dept. of Dental Medicine, Karolinska Institutet, Stockholm, Sweden. Electronic address: wedad.hammoudi@sll.se. 2. Dept. of Dental Medicine, Karolinska Institutet, Stockholm, Sweden; Scandinavian Centre for Orofacial Neuroscience (SCON), Denmark. 3. Dept. of Prosthetic Dentistry, Folktandvården Eastmaninstitutet, Stockholm, Sweden; Dept. of Dental Medicine, Karolinska Institutet, Stockholm, Sweden. 4. Dept. of Dental Medicine, Karolinska Institutet, Stockholm, Sweden; Scandinavian Centre for Orofacial Neuroscience (SCON), Denmark; Section of Orofacial Pain and Jaw Function, Department of Dentistry and Oral Health, Aarhus University, Aarhus, Denmark.
Abstract
OBJECTIVES: Explore a new approach to identify phenotypes of tooth wear (TW) patients using an unsupervised cluster analysis model, based on demographic, self-report, clinical, salivary and electromyographic (EMG) findings. METHODS: Data was collected for 34 variables from 125 patients, aged 17-65 years, with a TW index > grade 2. Demographic information and presumed risk factors for chemical and mechanical TW were collected. A 14-item stress scale was completed and salivary flow rates, pH and buffer capacity were measured. Sleep bruxism was assessed with a portable single channel EMG device. RESULTS: The final cluster model comprised 16 variables and 103 patients and indicated two groups of TW patients; 61 participants in cluster A and 42 in cluster B. Cluster assignment was determined by several presumed mechanical risk factors and diseases affecting saliva. Cluster B had the highest percentage of sleep bruxism self-reports (A 1.6%, B 92.9%, p ≤ 0.001), awake bruxism self-reports (A 45.9%, B 85.7%, p ≤ 0.001), heavy sport exercises (A 1.6%, B 21.4%, p = 0.001); and highest percentage of diseases affecting saliva (A 13.1%, B 47.6%, p ≤ 0.001). A notable finding was the lack of significant differences between clusters in many other presumed risk factors for mechanical and chemical TW. CONCLUSION: TW patients can be clustered in at least two groups with different phenotypic characteristics but also with a large degree of overlap. Based on this type of algorithm, tools for clinical application may be developed and underpin TW classification and treatment planning in the future.
OBJECTIVES: Explore a new approach to identify phenotypes of tooth wear (TW) patients using an unsupervised cluster analysis model, based on demographic, self-report, clinical, salivary and electromyographic (EMG) findings. METHODS: Data was collected for 34 variables from 125 patients, aged 17-65 years, with a TW index > grade 2. Demographic information and presumed risk factors for chemical and mechanical TW were collected. A 14-item stress scale was completed and salivary flow rates, pH and buffer capacity were measured. Sleep bruxism was assessed with a portable single channel EMG device. RESULTS: The final cluster model comprised 16 variables and 103 patients and indicated two groups of TW patients; 61 participants in cluster A and 42 in cluster B. Cluster assignment was determined by several presumed mechanical risk factors and diseases affecting saliva. Cluster B had the highest percentage of sleep bruxism self-reports (A 1.6%, B 92.9%, p ≤ 0.001), awake bruxism self-reports (A 45.9%, B 85.7%, p ≤ 0.001), heavy sport exercises (A 1.6%, B 21.4%, p = 0.001); and highest percentage of diseases affecting saliva (A 13.1%, B 47.6%, p ≤ 0.001). A notable finding was the lack of significant differences between clusters in many other presumed risk factors for mechanical and chemical TW. CONCLUSION: TW patients can be clustered in at least two groups with different phenotypic characteristics but also with a large degree of overlap. Based on this type of algorithm, tools for clinical application may be developed and underpin TW classification and treatment planning in the future.
Authors: Magdalini Thymi; Frank Lobbezoo; Ghizlane Aarab; Jari Ahlberg; Kazuyoshi Baba; Maria Clotilde Carra; Luigi M Gallo; Antoon De Laat; Daniele Manfredini; Gilles Lavigne; Peter Svensson Journal: J Oral Rehabil Date: 2021-05-02 Impact factor: 3.558