How bruxism develops from adolescence to early adulthood remains unclear. A previous database was revisited to evaluate the natural course of self-reported tooth grinding and clenching among young Finns aged 14-23 using four assessments. Overall, the self-reported frequencies of both grinding and clenching increased during the examination period: from 13.7% to 21.7% and from 9.2% to 14.8%, respectively. There were significant increases (without a statistically significant difference between genders) in both grinding (P = 0.002) and clenching (P = 0.015) between 15 and 23 years. A significant rise in grinding between 18 and 23 years was also found (P = 0.011). It is concluded that self-reported bruxism increases from adolescence to young adulthood. Moreover, there are large differences between individuals, and fluctuations may occur in the natural course of bruxism.

Study Objectives Sleep bruxism (SB) can cause damage on teeth, headache and severe pain affecting both sleep and daily functioning. Yet despite the growing interest into bruxism, the underlying clinically relevant biological mechanisms remain unresolved. The aim of our study was to understand biological mechanisms and clinical correlates of SB including previously reported disease associations. Methods We used data from the FinnGen release R9 (N=377,277 individuals) that are linked with Finnish hospital and primary care registries. We identified 12,297 (3.26%) individuals with International Classification of Diseases (ICD)-10 codes used for SB. In addition, we used logistic regression to examine the association between probable SB and its clinically diagnosed risk factors and comorbidities using ICD-10 codes. Furthermore, we examined medication purchases using prescription registry. Finally, we performed the first genome-wide association analysis for probable SB and computed genetic correlations using questionnaire, lifestyle and clinical traits. Results The genome-wide association analysis revealed one significant association: rs10193179 intronic to Myosin IIIB (MYO3B) gene. In addition, we observed phenotypic associations and high genetic correlations with pain diagnoses, sleep apnea, reflux disease, upper respiratory diseases, psychiatric traits and also their related medications such as antidepressants and sleep medication (P<1e-4 for each trait). Conclusions Our study provides a large-scale genetic framework to understand risk factors for SB and suggests potential biological mechanisms. Furthermore, our work strengthens the important earlier work that highlights SB as a trait that is associated with multiple axes of health. As part of this study, we provide genome-wide summary statistics that we hope will be useful for the scientific community studying SB.