ABSTRACT
Background
Insomnia is associated with various systemic conditions; however, its relationship with structural dental outcomes—particularly tooth wear and tooth fracture—has not been well defined.
Objectives
To investigate the association between clinically diagnosed insomnia and the incidence of tooth wear and fracture in a nationwide population‐based cohort.
Methods
Data were obtained from the Korean National Health Insurance Service—National Sample Cohort. Adults aged ≥ 20 years who underwent health screening between 2011 and 2014 were included. Insomnia was defined using the International Classification of Diseases, 10th Revision codes, and prescription records of hypnotics. Two separate cohorts were constructed for tooth wear and fracture outcomes. Individuals with prior diagnoses of bruxism, temporomandibular disorders, malignant neoplasms, certain psychiatric or neurological disorders, or insomnia during the washout period were excluded. Propensity score matching (1:3) was applied, and hazard ratios (HRs) with 95% confidence intervals (CIs) were estimated using the Cox proportional hazards models.
Results
After matching, 81 096 participants were included in the tooth wear cohort and 82 256 in the tooth fracture cohort. Individuals with insomnia exhibited significantly higher incidence rates of tooth wear (29.02 vs. 26.27 per 1000 person‐years; adjusted HR: 1.10, 95% CI: 1.06–1.15) and tooth fracture (16.70 vs. 13.63 per 1000 person‐years; adjusted HR: 1.20, 95% CI: 1.13–1.26) than controls.
Conclusions
Clinically diagnosed insomnia was modestly but significantly associated with increased risks of tooth wear and fracture, underscoring the need for early oral health evaluation and coordinated care between dental and sleep medicine professionals.
Keywords: bruxism, cohort study, insomnia, sleep disorders, tooth fracture, tooth wear
In this nationwide population‐based cohort study, insomnia was associated with increased risks of tooth wear and tooth fracture. These findings suggest that insomnia may have clinically relevant implications for oral health.
1. Introduction
Sleep disorders are increasingly recognized as significant public health concerns that affect both physical and mental health [1]. These disorders, characterized by disruptions in sleep quality, duration or regulation, have been associated with chronic pain, cardiovascular and neurodegenerative diseases and gastrointestinal dysfunction [1]. Such conditions reduce the quality of life and increase the risk of mortality, imposing substantial socioeconomic burdens on individuals and society [2, 3, 4].
The global prevalence of sleep disorders has substantially increased over the past decade [2, 5]. In the United States, the prevalence rates increased from approximately 20% to 41.7% between 2011 and 2016 [2]. In Japan, 13.5% of adults were affected in 2016 [6], while in South Korea, the proportion of diagnosed cases nearly doubled from 9.4% in 2011 to 17.3% in 2020 [2]. These trends may reflect a longer life expectancy, improved awareness and diagnosis and rising rates of chronic diseases, including cancer, obesity and neurodegenerative conditions [2]. Moreover, during periods of global stress, such as the COVID‐19 pandemic, the prevalence of sleep disturbances increased significantly [7].
While the systemic effects of sleep disorders are well established, growing evidence also indicates their adverse impact on oral health. Previous studies have shown that chronic sleep disturbances are associated with periodontal disease, temporomandibular disorders (TMDs), oral mucosal inflammation and dental caries [1]. These associations may be mediated by autonomic imbalance [8], dysregulation of the hypothalamic–pituitary–adrenal (HPA) axis [9], impaired immune responses, impaired neuromuscular coordination and systemic inflammation—as well as alterations in the intraoral environment, including reduced salivary flow [1, 10, 11, 12]. Collectively, these changes may increase the vulnerability to various adverse oral conditions, including structural dental deterioration [11].
Among the various oral conditions potentially linked to sleep disturbances, tooth wear and fracture are of particular clinical relevance because of their irreversible nature and substantial aesthetic and functional consequences [12]. Tooth wear—encompassing attrition, abrasion, abfraction and erosion—is a cumulative process that increases with age and may progress to a pathological state when it exceeds the adaptive capacity of the stomatognathic system [13]. In contrast, tooth fracture represents an acute structural failure that may require restorative treatment or, in severe cases, tooth extraction [14].
Traditionally, tooth wear and fractures have been attributed primarily to mechanical and chemical factors. However, recent literature suggests that behavioural and systemic conditions—particularly sleep‐related problems—may also contribute to their development [15, 16, 17]. Nevertheless, findings from prior research have been inconsistent due to heterogeneity in study designs, diagnostic criteria and assessment methods, as well as the inherently multifactorial nature of tooth wear and fractures. Consequently, the relationship between clinically diagnosed insomnia and structural tooth damage remains insufficiently established. Given these considerations, the present study aimed to investigate the association between clinically diagnosed insomnia and the incidence of tooth wear and fracture using the Korean Health Insurance Service–National Sample Cohort (NHIS‐NSC), a nationally representative claims database. Our working hypothesis was that adults with clinically diagnosed insomnia would exhibit a higher incidence of tooth wear and fractures than those without insomnia.
2. Materials and Methods
2.1. Data Source
Data were obtained from the NHIS, which oversees a universal and mandatory healthcare system covering over 97% of the Korean population. The NHIS database has contained comprehensive longitudinal information on demographic characteristics, insurance status, healthcare utilization, diagnoses coded according to the International Classification of Diseases, 10th Revision (ICD‐10), medical procedures, prescription records and mortality data since 2002 [18]. Additionally, the NHIS offers a standardized health screening programme conducted every 1–2 years for all insured adults aged ≥ 40 years and for employed individuals aged ≥ 20 years. This programme collects a wide range of clinical and behavioural health information, including anthropometric measurements, blood pressure, laboratory test results, smoking status, alcohol consumption and physical activity levels.
For this study, data were drawn from the NHIS‐NSC, a population‐based cohort established by the NHIS [19]. The NHIS‐NSC comprises approximately one million individuals, representing 2% of the total Korean population in 2002. Participants were selected through stratified random sampling based on age, sex, residential area and insurance eligibility. The cohort was followed up annually until 2019 using health claims, enrolment data and death records.
This study was approved by the Institutional Review Board (IRB) of Seoul National University Bundang Hospital (IRB No. X‐2018‐705‐901) and was conducted in accordance with the ethical standards of the Declaration of Helsinki. As the analysis was based on fully anonymized secondary data provided by the NHIS, the IRB waived the requirement for informed consent in accordance with the national data use guidelines.
2.2. Study Population and Cohort Selection
Between January 1, 2011, and December 31, 2014, a total of 511 396 adults aged ≥ 20 years who participated in at least one national health screening examination were identified. The index date was defined as the most recent health screening date for each individual between 2011 and 2014.
The exposure of interest was clinically diagnosed insomnia. The insomnia group consisted of individuals with at least one medical claim coded with ICD‐10 G47.0 (disorders of initiating and maintaining sleep) or F51.0 (nonorganic insomnia), recorded as either a principal or additional diagnosis. To enhance diagnostic specificity, individuals were required to have a prescription for hypnotic medications lasting more than 7 consecutive days. These criteria were based on previous Korean studies investigating the insomnia prevalence and incidence [20].
According to the Korean Clinical Practice Guideline for insomnia, 19 pharmacologic treatments are listed, of which 12 have been approved by the Korean Ministry of Food and Drug Safety [21]. To reduce misclassification, medications commonly used for other indications, such as anxiety, delirium, pain or depression, were excluded. In particular, agents such as antihistamines were excluded because they are frequently prescribed for non‐insomnia conditions such as allergic disorders. Furthermore, certain agents (e.g., melatonin and eszopiclone) were excluded because they were not approved or commercially available in Korea during the cohort inclusion period. Ultimately, only six hypnotic agents (doxepin, flunitrazepam, flurazepam, triazolam, zolpidem and trazodone) were included in the analysis [20]. The control group included individuals with no record of insomnia diagnosis or hypnotic prescriptions during the study period.
To evaluate the association between insomnia and two distinct structural dental outcomes, we constructed two outcome‐specific analytic cohorts using identical inclusion and exclusion criteria, differing only in the respective outcome definition (K03.00–K03.19 for tooth wear and S02.52–S02.59 for tooth fracture). Incidence cases were identified based on a single newly recorded diagnostic claim without linkage to procedure codes, consistent with standard NHIS‐based dental epidemiologic algorithms. This definition approach is widely used in practice for identifying dental diagnoses in NHIS claims data, and the same method was applied in the present study. Additional validation of these diagnostic algorithms was performed, and detailed metrics are provided in Supporting Information. For tooth wear, the diagnostic algorithm demonstrated a sensitivity of 83.6%, specificity of 91.1%, positive predictive value (PPV) of 92.0%, negative predictive value (NPV) of 82.0% and an overall accuracy of 87.0%. For tooth fractures, the algorithm showed a sensitivity of 91.6%, specificity of 79.5%, PPV of 76.0%, NPV of 93.0% and an overall accuracy of 84.5%.
Participants were excluded if they met any of the following conditions: (1) diagnosis of malignant neoplasms (ICD‐10 codes C00–C97) from 2002 to 2014; (2) prior diagnosis of bruxism (F51.8, F45.8), TMD (K07.6) or the respective outcome condition before the index date; (3) diagnosis of anxiety disorders (F40–F42), depressive disorders (F32–F34) or epilepsy (G41) at any time before the index date; and (4) diagnosis of insomnia (F51.0 or G47.0) before or during the index year, which was considered the washout period. To strictly identify incident cases, we excluded individuals who had any diagnosis of insomnia recorded at any time prior to the index date, effectively serving as a washout period spanning from 2002 to the index date. After exclusions, 351 689 participants were included in the tooth wear analysis (insomnia group = 20 336; control group = 331 353), and 355 558 participants were included in the tooth fracture analysis (insomnia group = 20 625; control group = 334 933).
Propensity score matching (PSM) was conducted separately for each cohort at a 1:3 ratio using a greedy nearest‐neighbour algorithm with a 0.2 calliper. Matching was based on key baseline characteristics, including age, sex, household income, body mass index (BMI), smoking status and alcohol consumption. These variables were selected because they represent well‐established risk factors for both insomnia and dental outcomes, such as tooth wear and fracture, and therefore constitute key potential confounders requiring balance between groups. A 1:3 matching ratio was chosen to improve statistical efficiency while preserving an adequate pool of eligible controls without compromising the covariate balance. Participants were followed from the index date until the development of the respective outcome, death or December 31, 2019, whichever occurred first see (Figure 1 (a) for the tooth wear cohort and (b) for the tooth fracture cohort).
FIGURE 1.
Flowchart of the study population selection. (A) Selection of participants in the tooth wear cohort. (B) Selection of participants in the tooth fracture cohort.
2.3. Variable Definition and Primary Outcome
Baseline characteristics, including age, sex, household income, BMI, smoking status and alcohol consumption, were evaluated at the index date. Comorbidities were identified using ICD‐10 codes recorded 1 year prior to the index date. These included hypertension (I10–I15), hyperthyroidism (E05), hypothyroidism (E03), diabetes mellitus (E10–E14), dyslipidaemia (E78), myocardial infarction (I21–I25), stroke (I60–I63), anaemia (D46, D50–D53, D55–D64, D74), asthma (J45), pneumonia (J12–J18) and chronic obstructive pulmonary disease (COPD; J43–J44).
The primary outcomes were the incidence of tooth wear and fracture, which were analysed separately using the diagnostic algorithms described above. Participants were followed from the index date until the first occurrence of the respective dental outcome, death or December 31, 2019, whichever occurred first. Because the index date corresponded to each participant's most recent health screening examination, which occurred variably between 2011 and 2014, the exact mean follow‐up duration could not be calculated uniformly. Thus, the individual follow‐up periods ranged from approximately 6 to 9 years, depending on the year of the index health screening.
2.4. Statistical Analyses
Baseline characteristics were compared between the insomnia and control groups using Student's t‐tests for continuous variables and chi‐square tests for categorical variables. The incidence rates of tooth wear and fracture were calculated as the number of new cases per 1000 person‐years (PY). Binomial proportion tests were used to assess differences in incidence rates between the groups.
Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between insomnia and the risk of tooth wear or fracture. Multivariable models were adjusted for age, sex, income, BMI, smoking status, alcohol consumption and comorbidities.
PSM at a 1:3 ratio was conducted using nearest neighbour matching with a calliper of 0.2. Covariate balance before and after matching was assessed using standardized mean differences (SMDs), with an SMD of < 0.1 indicating a good balance. Cox regression analyses were repeated for the matched cohorts to ensure robustness.
Kaplan–Meier curves were generated to depict the cumulative incidence of each outcome stratified by insomnia status. Group differences were evaluated using the log‐rank test.
As a sensitivity analysis, participants were additionally categorized into three groups: (1) no insomnia, (2) untreated insomnia (diagnosis without hypnotic prescription) and (3) treated insomnia (diagnosis with hypnotic prescription). In the primary analysis, individuals with an insomnia diagnosis but no hypnotic prescription were excluded from both the insomnia and control groups. For the sensitivity analysis, these individuals were reclassified as the untreated insomnia group to allow a more comprehensive evaluation of treatment effects. Multivariable Cox proportional hazards models were applied to estimate HRs for tooth wear and tooth fracture across these groups. A direct comparison between treated and untreated insomnia groups was also performed to assess whether pharmacologic treatment modified the observed associations.
All statistical analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA), and a two‐sided p‐value < 0.05 was considered statistically significant.
3. Results
3.1. Baseline Characteristics
Table 1 summarizes the baseline characteristics of the participants in the tooth‐wear cohort. After 1:3 PSM, 81 096 participants (20 274 with insomnia and 60 822 controls) were included in the matched samples. Similarly, Table 2 presents the baseline characteristics of the tooth fracture cohort, comprising 82 256 participants (20 564 with insomnia and 61 692 controls). In both cohorts, matching achieved an adequate balance across key variables, including age, sex, income, BMI, smoking status and alcohol consumption. In both cohorts, matching achieved adequate balance across the matched variables (age, sex, income, BMI, smoking status and alcohol consumption), with all SMDs < 0.1. Comorbidities, which were not included in the matching algorithm but adjusted for in the multivariable models, retained expected differences between groups (Tables 1, 2). However, some comorbidities remained more prevalent in the insomnia group, reflecting a greater burden of chronic disease.
TABLE 1.
Baseline characteristics of participants according to tooth wear status, before and after propensity score matching.
| Variable | Total | Before PSM (n = 351 689) | p | After PSM (n = 81 096) | SMD | ||
|---|---|---|---|---|---|---|---|
| Insomnia (−) | Insomnia (+) | Insomnia (−) | Insomnia (+) | ||||
| No. of participants (%) | 351 689 (100.0) | 331 353 (94.2) | 20 336 (5.8) | 60 822 (75.0) | 20 274 (25.0) | ||
| Age | 48.13 ± 14.58 | 47.46 ± 14.36 | 59.09 ± 13.78 | < 0.0001 | 59.18 ± 13.52 | 59.09 ± 13.78 | 0.007 |
| 20–29 | 37 213 (10.6) | 36 711 (11.1) | 502 (2.5) | 1276 (2.1) | 440 (2.2) | ||
| 30–39 | 62 496 (17.8) | 61 371 (18.5) | 1125 (5.5) | 3190 (5.2) | 1125 (5.6) | ||
| 40–49 | 94 898 (27.0) | 91 653 (27.7) | 3245 (16.0) | 9353 (15.4) | 3245 (16.0) | ||
| 50–59 | 77 560 (22.1) | 72 262 (21.8) | 5298 (26.1) | 16 241 (26.7) | 5298 (26.1) | ||
| 60–69 | 44 858 (14.6) | 40 229 (14.4) | 4629 (13.8) | 14 502 (13.5) | 4629 (13.8) | ||
| ≥ 70 | 34 664 (9.9) | 29 127 (8.8) | 5537 (27.2) | 16 260 (26.7) | 5537 (27.3) | ||
| Sex | < 0.0001 | 0.011 | |||||
| Male | 179 785 (51.1) | 173 108 (52.2) | 6677 (32.8) | 19 667 (32.3) | 6668 (32.9) | ||
| Female | 171 904 (48.9) | 158 245 (47.8) | 13 659 (67.2) | 41 155 (67.7) | 13 606 (67.1) | ||
| Household income | < 0.0001 | −0.053 | |||||
| Insured | 347 157 (98.7) | 327 577 (98.9) | 19 580 (96.3) | 59 047 (97.1) | 19 518 (96.3) | ||
| Medical aid | 4532 (1.3) | 3776 (1.1) | 756 (3.7) | 1775 (2.9) | 756 (3.7) | ||
| BMI | 23.85 ± 3.45 | 23.85 ± 3.45 | 23.85 ± 3.4 | 0.0415 | 23.87 ± 3.26 | 23.86 ± 3.40 | −0.004 |
| < 18.5 | 13 999 (4.0) | 13 197 (4.0) | 802 (3.9) | 1980 (3.3) | 785 (3.9) | ||
| 18.5–22.9 | 129 420 (36.8) | 122 063 (36.8) | 7357 (36.2) | 22 005 (36.2) | 7322 (36.1) | ||
| 23.0–24.9 | 83 410 (23.7) | 78 425 (23.7) | 4985 (24.5) | 15 187 (25.0) | 4979 (24.6) | ||
| ≥ 25.0 | 124 860 (35.5) | 117 668 (35.5) | 7192 (35.4) | 21 650 (35.6) | 7188 (35.5) | ||
| Smoking | < 0.0001 | 0.035 | |||||
| Non‐smoker | 213 532 (60.7) | 198 916 (60.0) | 14 616 (71.9) | 44 772 (73.6) | 14 555 (71.8) | ||
| Ex‐smoker | 49 676 (14.1) | 47 415 (14.3) | 2261 (11.1) | 6313 (10.4) | 2260 (11.2) | ||
| Current smoker | 88 481 (25.2) | 85 022 (25.7) | 3459 (17.0) | 9737 (16.0) | 3459 (17.1) | ||
| Alcohol | < 0.0001 | 0.007 | |||||
| Non‐drinker | 76 330 (21.7) | 73 871 (22.3) | 2459 (12.1) | 7325 (12.0) | 2459 (12.1) | ||
| 1–2 sessions/week | 252 494 (71.8) | 235 759 (71.2) | 16 735 (82.3) | 50 326 (82.7) | 16 674 (82.2) | ||
| ≥ 3 sessions/week | 22 865 (6.5) | 21 723 (6.6) | 1142 (5.6) | 3171 (5.2) | 1141 (5.6) | ||
| Comorbidity | |||||||
| Hypertension | 77 291 (22.0) | 67 762 (20.5) | 9529 (46.9) | < 0.0001 | 22 776 (37.5) | 9480 (46.8) | |
| Hyperthyroid | 8881 (2.5) | 7741 (2.3) | 1140 (5.6) | < 0.0001 | 1837 (3.0) | 1140 (5.6) | |
| Hypothyroid | 14 954 (4.3) | 12 999 (3.9) | 1955 (9.6) | < 0.0001 | 3436 (5.7) | 1949 (9.6) | |
| Diabetes mellitus | 32 576 (9.3) | 28 210 (8.5) | 4366 (21.5) | < 0.0001 | 9406 (15.5) | 4350 (21.5) | |
| Dyslipidaemia | 59 472 (16.9) | 51 544 (15.6) | 7928 (39.0) | < 0.0001 | 16 609 (27.3) | 7910 (39.0) | |
| Myocardial infarction | 6642 (1.9) | 5583 (1.7) | 1059 (5.2) | < 0.0001 | 1982 (3.3) | 1056 (5.2) | |
| Stroke | 8228 (2.3) | 6588 (2.0) | 1640 (8.1) | < 0.0001 | 2685 (4.4) | 1628 (8.0) | |
| Anaemia | 41 904 (11.9) | 36 772 (11.1) | 5132 (25.2) | < 0.0001 | 9132 (15.0) | 5099 (25.2) | |
| Asthma | 73 364 (20.9) | 65 861 (19.9) | 7503 (36.9) | < 0.0001 | 15 080 (24.8) | 7477 (36.9) | |
| Pneumonia | 26 624 (7.6) | 23 305 (7.0) | 3319 (16.3) | < 0.0001 | 5649 (9.3) | 3297 (16.3) | |
| COPD | 10 772 (3.1) | 8906 (2.7) | 1866 (9.2) | < 0.0001 | 3197 (5.3) | 1852 (9.1) | |
Note: 1:3 ratio propensity score matching (calliper: 0.2) using age, sex, household income, BMI, smoking and alcohol consumption. Comorbidities were not included in the matching algorithms.
Abbreviations: BMI, Body mass index; COPD, Chronic obstructive pulmonary disease.
TABLE 2.
Baseline characteristics of participants according to tooth fracture status, before and after propensity score matching.
| Variable | Total | Before PSM (n = 355 558) | p | After PSM (n = 82 256) | SMD | ||
|---|---|---|---|---|---|---|---|
| Insomnia (−) | Insomnia (+) | Insomnia (−) | Insomnia (+) | ||||
| No. of participants (%) | 355 558 (100.0) | 334 933 (94.2) | 20 625 (5.8) | 61 692 (75.0) | 20 564 (25.0) | ||
| Age | 48.17 ± 14.53 | 47.5 ± 14.31 | 59.06 ± 13.73 | < 0.0001 | 59.13 ± 13.43 | 59.06 ± 13.73 | 0.005 |
| 20–29 | 37 242 (10.5) | 36 745 (11.0) | 497 (2.4) | 1252 (2.0) | 436 (2.1) | ||
| 30–39 | 62 676 (17.6) | 61 548 (18.4) | 1128 (5.5) | 3189 (5.2) | 1128 (5.5) | ||
| 40–49 | 96 222 (27.1) | 92 919 (27.7) | 3303 (16.0) | 9576 (15.5) | 3303 (16.1) | ||
| 50–59 | 79 143 (22.3) | 73 725 (22.0) | 5418 (26.3) | 16 519 (26.8) | 5418 (26.4) | ||
| 60–69 | 45 515 (14.5) | 40 801 (14.3) | 4714 (13.7) | 14 854 (13.4) | 4714 (13.7) | ||
| ≥ 70 | 34 760 (9.8) | 29 195 (8.7) | 5565 (27.0) | 16 302 (26.4) | 5565 (27.1) | ||
| Sex | < 0.0001 | 0.015 | |||||
| Male | 181 484 (51.0) | 174 700 (52.2) | 6784 (32.9) | 19 884 (32.2) | 6775 (33.0) | ||
| Female | 174 074 (49.0) | 160 233 (47.8) | 13 841 (67.1) | 41 808 (67.8) | 13 789 (67.1) | ||
| Household income | < 0.0001 | −0.054 | |||||
| Insured | 350 942 (98.7) | 331 087 (98.9) | 19 855 (96.3) | 59 895 (97.1) | 19 794 (96.3) | ||
| Medical aid | 4616 (1.3) | 3846 (1.2) | 770 (3.7) | 1797 (2.9) | 770 (3.7) | ||
| BMI | 23.84 ± 3.45 | 23.84 ± 3.45 | 23.85 ± 3.39 | 0.0309 | 23.86 ± 3.24 | 23.86 ± 3.39 | −0.002 |
| < 18.5 | 14 130 (4.0) | 13 325 (4.0) | 805 (3.9) | 1968 (3.2) | 788 (3.8) | ||
| 18.5–22.9 | 131 066 (36.9) | 123 610 (36.9) | 7456 (36.2) | 22 391 (36.3) | 7421 (36.1) | ||
| 23.0–24.9 | 84 393 (23.7) | 79 337 (23.7) | 5056 (24.5) | 15 330 (24.9) | 5050 (24.6) | ||
| ≥ 25.0 | 125 969 (35.4) | 118 661 (35.4) | 7308 (35.4) | 22 003 (35.7) | 7305 (35.5) | ||
| Smoking | < 0.0001 | 0.038 | |||||
| Non‐smoker | 216 117 (60.8) | 201 281 (60.1) | 14 836 (71.9) | 45 549 (73.8) | 14 776 (71.9) | ||
| Ex‐smoker | 50 371 (14.2) | 48 070 (14.4) | 2301 (11.2) | 6370 (10.3) | 2300 (11.2) | ||
| Current smoker | 89 070 (25.1) | 85 582 (25.6) | 3488 (16.9) | 9773 (15.8) | 3488 (17.0) | ||
| Alcohol | < 0.0001 | 0.005 | |||||
| Non‐drinker | 77 085 (21.7) | 74 604 (22.3) | 2481 (12.0) | 7383 (12.0) | 2481 (12.1) | ||
| 1–2 sessions/week | 255 446 (71.8) | 238 460 (71.2) | 16 986 (82.4) | 51 048 (82.8) | 16 926 (82.3) | ||
| ≥ 3 sessions/week | 23 027 (6.5) | 21 869 (6.5) | 1158 (5.6) | 3261 (5.3) | 1157 (5.6) | ||
| Comorbidity | |||||||
| Hypertension | 78 050 (22.0) | 68 395 (20.4) | 9655 (46.8) | < 0.0001 | 23 076 (37.4) | 9608 (46.7) | |
| Hyperthyroid | 9026 (2.5) | 7857 (2.4) | 1169 (5.7) | < 0.0001 | 1950 (3.2) | 1169 (5.7) | |
| Hypothyroid | 15 212 (4.3) | 13 214 (4.0) | 1998 (9.7) | < 0.0001 | 3572 (5.8) | 1992 (9.7) | |
| Diabetes mellitus | 32 909 (9.3) | 28 494 (8.5) | 4415 (21.4) | < 0.0001 | 9550 (15.5) | 4399 (21.4) | |
| Dyslipidaemia | 60 232 (16.9) | 52 175 (15.6) | 8057 (39.1) | < 0.0001 | 16 948 (27.5) | 8040 (39.1) | |
| Myocardial infarction | 6667 (1.9) | 5602 (1.7) | 1065 (5.2) | < 0.0001 | 2016 (3.3) | 1061 (5.2) | |
| Stroke | 8298 (2.3) | 6644 (2.0) | 1654 (8.0) | < 0.0001 | 2833 (4.6) | 1643 (8.0) | |
| Anaemia | 42 444 (11.9) | 37 225 (11.1) | 5219 (25.3) | < 0.0001 | 9277 (15.0) | 5187 (25.2) | |
| Asthma | 74 308 (20.9) | 66 689 (19.9) | 7619 (36.9) | < 0.0001 | 15 400 (25.0) | 7594 (36.9) | |
| Pneumonia | 26 959 (7.6) | 23 614 (7.1) | 3345 (16.2) | < 0.0001 | 5761 (9.3) | 3323 (16.2) | |
| COPD | 10 919 (3.1) | 9025 (2.7) | 1894 (9.2) | < 0.0001 | 3178 (5.2) | 1880 (9.1) | |
Note: 1:3 ratio propensity score matching (calliper: 0.2) using age, sex, household income, BMI, smoking and alcohol consumption. Comorbidities were not included in the matching algorithm.
Abbreviations: BMI, Body mass index; COPD, Chronic obstructive pulmonary disease.
3.2. Association Between Insomnia and Risk of Tooth Wear
To examine whether clinically diagnosed insomnia was associated with an increased risk of tooth wear, we compared the incidence of tooth wear between the insomnia and control groups. Table 3 summarizes the incidence of tooth wear in unmatched and matched cohorts. In the unmatched cohort (N = 351 689), the incidence rate of tooth wear was higher in the insomnia group than in the control group (28.98 vs. 25.03 per 1000 PY). The crude HR was 1.16 (95% CI: 1.12–1.21; p < 0.0001), and this association remained statistically significant after adjustment for covariates (adjusted HR: 1.07; 95% CI: 1.03–1.11; p = 0.0005). Detailed results of the crude and adjusted Cox regression analyses, including covariate‐specific estimates before and after propensity score matching, are presented in Table S1‐1 and S1‐2.
TABLE 3.
Association between insomnia and risk of tooth wear, before and after propensity score matching.
| Number of participants | Event rate (%, 95% CI) | Person‐years | Incidence rate (per 1000 PY) | Adjusted HR | p | |
|---|---|---|---|---|---|---|
| Before PSM | ||||||
| All participants | 351 689 | 14.11 (14.0, 14.2) | 1965860.16 | 25.25 | ||
| Insomnia diagnosis | ||||||
| No | 331 353 | 14.03 (13.9, 14.2) | 1857204.01 | 25.03 | 1 (ref) | |
| Yes | 20 336 | 15.48 (15.0, 16.0) | 108656.15 | 28.98 | 1.07 (1.03, 1.11) | 0.001 |
| After PSM | ||||||
| All participants | 81 096 | 14.87 (14.6, 15.1) | 447880.35 | 26.93 | ||
| Insomnia diagnosis | ||||||
| No | 60 822 | 14.66 (14.4, 14.9) | 339386.82 | 26.27 | 1 (ref) | |
| Yes | 20 274 | 15.53 (15.0, 16.0) | 108493.53 | 29.02 | 1.10 (1.06, 1.15) | < 0.001 |
Note: The multivariable model was adjusted for sex, age, household income, body mass index, smoking habit, drinking habit, hypertension, hyperthyroidism, hypothyroidism, diabetes mellitus, myocardial infarction, stroke, anaemia, asthma, pneumonia, chronic obstructive pulmonary disease and insomnia.
Abbreviations: CI, confidence interval; HR, hazard ratio.
In the matched cohort (N = 81 096), the incidence rate of tooth wear remained higher in the insomnia group (29.02 vs. 26.27 per 1000 PY). The crude HR was 1.11 (95% CI: 1.06–1.15, p < 0.0001), closely aligning with the adjusted estimate (adjusted HR of 1.10; 95% CI: 1.06–1.15; p < 0.001), indicating that the association persisted even after balancing baseline characteristics. Kaplan–Meier survival analysis (Figure 2) also showed a consistently higher cumulative incidence of tooth wear in the insomnia group (log‐rank p < 0.001).
FIGURE 2.
Cumulative incidence of (a) tooth wear and (b) tooth fracture according to insomnia status. Kaplan–Meier survival curves comparing the cumulative incidence between the insomnia and non‐insomnia groups. The insomnia group showed a significantly higher incidence of both tooth wear (a) and tooth fracture (b) over the follow‐up period (log‐rank test, p < 0.001). Censoring occurred at death or at the administrative end of follow‐up (December 31, 2019) for participants who did not experience the respective outcomes. The stepwise appearance of the curves reflects the structure of the NHIS claims data, in which event times are recorded at discrete intervals rather than exact dates.
3.3. Association Between Insomnia and Risk of Tooth Fracture
To examine whether clinically diagnosed insomnia was associated with an increased risk of tooth fracture, we compared the incidence of tooth wear between the insomnia and control groups. Table 4 shows the incidence of tooth fractures in the unmatched and matched cohorts. In the unmatched sample (N = 355 558), individuals with insomnia had a significantly higher incidence of tooth fractures than those without insomnia (16.68 vs. 12.54 per 1000 PY). The crude HR was 1.34 (95% CI: 1.28–1.40; p < 0.0001), and this association remained significant after adjusting for relevant covariates (adjusted HR: 1.19; 95% CI: 1.13–1.25; p < 0.001). Detailed results of the crude and adjusted Cox regression analyses, including covariate‐specific estimates before and after propensity score matching, are presented in Table S2‐1 and S2‐2.
TABLE 4.
Association between insomnia and risk of tooth fracture, before and after propensity score matching.
| Number of participants | Event rate (%, 95% CI) | Person‐years | Incidence rate (per 1000 PY) | Adjusted HR | p | |
|---|---|---|---|---|---|---|
| Before PSM | ||||||
| All participants | 355 558 | 7.39 (7.3, 7.47) | 2056892.45 | 12.77 | ||
| Insomnia diagnosis | ||||||
| No | 334 933 | 7.27 (7.19, 7.36) | 1942593.08 | 12.54 | 1 (ref) | |
| Yes | 20 625 | 9.24 (8.85, 9.64) | 114299.37 | 16.68 | 1.19 (1.13, 1.25) | < 0.001 |
| After PSM | ||||||
| All participants | 82 256 | 8.23 (8.04, 8.42) | 471015.3 | 14.37 | ||
| Insomnia diagnosis | ||||||
| No | 61 692 | 7.88 (7.67, 8.1) | 356881.53 | 13.63 | 1 (ref) | |
| Yes | 20 564 | 9.27 (8.87, 9.66) | 114133.77 | 16.70 | 1.20 (1.13, 1.26) | < 0.001 |
Note: The multivariable model was adjusted for sex, age, household income, body mass index, smoking habit, drinking habit, hypertension, hyperthyroidism, hypothyroidism, diabetes mellitus, myocardial infarction, stroke, anaemia, asthma, pneumonia, chronic obstructive pulmonary disease and insomnia.
Abbreviations: CI, confidence interval; HR, hazard ratio.
Following 1:3 PSM, the matched cohort comprised 82 256 individuals (20 564 with insomnia and 61 692 controls). The incidence rate of tooth fractures remained elevated in the insomnia group (16.70 vs. 13.63 per 1000 PY), with event rates of 9.27 (95% CI: 8.87–9.66) and 7.88 (95% CI: 7.67–8.10), respectively. The crude HR after matching was 1.23 (95% CI: 1.17–1.30; p < 0.0001), which closely aligned with the adjusted estimate (adjusted HR: 1.19; 95% CI: 1.13–1.25; p < 0.0001). Kaplan–Meier curves in Figure 2 illustrate the higher cumulative incidence of tooth fractures in the insomnia group, with significant differences observed in both unmatched and matched cohorts (log‐rank, p < 0.001).
3.4. Sensitivity Analyses According to Treatment Status
In additional analyses stratified by treatment status, both untreated insomnia and treated insomnia groups demonstrated significantly increased risks of tooth wear and fracture compared with individuals without insomnia (Table S3‐1). However, direct comparison between treated and untreated insomnia groups revealed no statistically significant differences in either outcome (Table S3‐2).
4. Discussion
In this large‐scale population‐based study, individuals with clinically diagnosed insomnia exhibited a consistently higher incidence of both tooth wear and fracture than those without insomnia. The magnitude of the association was modest but robust; insomnia was associated with a 10% higher risk of tooth wear and a 19% higher risk of tooth fracture after multivariable adjustment. These findings were stable across crude, adjusted and PSM analyses, consistent with our a priori hypothesis.
Previous studies have demonstrated associations between sleep disorders and various oral health outcomes, including periodontal disease, dental caries and tooth loss [10, 11]. Although many studies have explored the effects of sleep disorders on oral health, most have focused on conditions such as sleep bruxism, obstructive sleep apnoea (OSA) or gastroesophageal reflux disease (GERD)—which are commonly implicated in the pathogenesis of tooth wear and fracture [17, 22, 23]. Conversely, relatively few studies have directly examined the impact of insomnia—a distinct and highly prevalent sleep disorder [24]—on structural dental damage, including tooth wear and fracture. Our study expands on this literature by leveraging a nationally representative dataset to demonstrate that clinically diagnosed insomnia is independently associated with an increased risk of both tooth wear and fractures. Collectively, these results suggest that insomnia may represent a population‐level risk correlate for structural dental damage, potentially increasing long‐term oral health vulnerability.
Several hypothesized mechanisms based on the existing literature can be considered to explain the association observed in this study between insomnia and structural dental damage. One such mechanism is the increased risk of falls and accidental injuries in individuals with insomnia. Insomnia can impair attention and slow reaction time, making affected individuals more susceptible to unexpected accidents in daily life [25, 26, 27], while hypnotic medications—such as benzodiazepines—may further heighten this vulnerability [28]. Importantly, prior studies have reported that insomnia is associated with an increased risk of falls, even in the absence of hypnotic use, and that this association becomes more pronounced with greater insomnia severity, suggesting that insomnia‐related impairment may directly contribute to accidental trauma [25]. Because falls are well‐established causes of dental injuries—including crown and root fractures—these pathways could partially explain the elevated fracture risk observed in our study.
The second potential mechanism involves insomnia‐related reductions in salivary secretion and xerostomia. Saliva plays a critical protective role in buffering, lubrication and enamel remineralization [29], and reductions in salivary volume or changes in its composition can compromise these protective functions [30]. Insomnia is associated with elevated cortisol levels and suppression of melatonin synthesis [31], physiological alterations that have been linked to impaired salivary gland function and decreases in both salivary output and flow rate [31]. In addition, medications frequently prescribed for insomnia—including benzodiazepines, tricyclic antidepressants and other sedative–hypnotics—are well‐known contributors to xerostomia [32, 33]. These salivary changes weaken the intraoral buffering and lubrication, rendering dental hard tissues more susceptible to mechanical and chemical wear [30].
The third plausible mechanism involves GERD. GERD affects an estimated 13% of the global population [34] and its burden has risen substantially over recent decades. It is frequently comorbid with sleep disturbances and may be exacerbated in individuals with insomnia [35]. GERD is a well‐established cause of chemical tooth wear, as recurrent nocturnal acid exposure softens the enamel surface [35]. In addition, several studies suggest that acid‐softened dental structures may become particularly vulnerable to mechanical loading, particularly masticatory muscle activity, which can accompany reflux‐related micro‐arousals [36]. Thus, GERD represents a biologically plausible mechanism that may partially account for the increased risk of tooth wear and fracture observed in individuals with insomnia.
The fourth potential mechanism involves neuromuscular and stress‐regulatory alterations commonly observed in individuals with insomnia. Chronic insomnia is characterized by a state of central and autonomic hyperarousal [8, 9, 37, 38]. This state may increase non‐rhythmic, tonic muscle tension rather than rhythmic masticatory muscle activity [35]. Such sustained static loading can contribute to abfraction and micro‐fractures, distinct from the mechanical wear caused by grinding [35, 39]. However, because the existing literature shows substantial heterogeneity in the assessment of insomnia and dental outcomes, and findings remain inconsistent across studies, this mechanism should not be regarded as a definitive explanation but as one of several plausible pathways that may account for the associations observed in this study [8, 15, 16].
The fifth potential mechanism involves systemic inflammation and alterations in immune and metabolic regulation, which are commonly observed in individuals with insomnia. Insomnia has been associated with elevated levels of inflammatory cytokines, such as IL‐6 and CRP, as well as reduced melatonin secretion, which may increase oxidative stress and impair antioxidant defence [40, 41]. These changes can weaken the homeostasis of the gingival and periodontal tissues and diminish the capacity for microdamage repair in the dental hard tissues [11, 41]. Several studies have reported a higher risk of periodontal disease among individuals with insomnia [11]. In addition, chronic systemic inflammation has been linked to temporomandibular joint osteoarthritis [42], which may further compromise the stability of occlusal force distribution and increase the vulnerability of dental structures. Moreover, pro‐inflammatory cytokines can promote osteoclastic activity and mineral loss, thereby reducing the strength of dental tissues and alveolar bone support [40, 41]. Collectively, these systemic and local alterations may decrease the overall resistance to occlusal loading and contribute to greater susceptibility to tooth wear and fracture.
These proposed mechanisms represent plausible pathways that may contribute to the observed association. However, current evidence across studies remains heterogeneous and sometimes conflicting; therefore, none of these mechanisms should be regarded as definitive.
The findings of this study have several implications for dental and medical practices. As insomnia is associated with an increased risk of tooth wear and fracture, even after extensive adjustment and matching, clinicians should consider the possibility of underlying sleep disturbances when encountering patients with unexplained or progressive tooth surface loss. Insomnia is common but often overlooked in dental settings [43, 44, 45, 46], and early oral manifestations—such as enamel wear, accelerated wear or repeated fractures—may serve as indirect indicators of sleep‐related physiological stress [1].
Patients who are older or who have psychiatric, cardiometabolic or chronic medical conditions may particularly benefit from screening for insomnia when dental structural damage is present [12]. Incorporating brief sleep assessments or validated questionnaires into the routine evaluation of tooth wear or fracture may help identify high‐risk individuals and facilitate timely referral to a sleep specialist. Interdisciplinary collaboration between dentistry and sleep medicine may enhance early detection and allow more comprehensive management.
Despite its strengths, including a large, nationally representative cohort and robust analytic methodology, this study has some limitations. First, as with all claims‐based studies, the use of administrative data limits the ability to capture the clinical severity and detailed characteristics of tooth wear and fractures. Diagnostic codes do not include objective clinical information, such as intraoral visual assessments, digital scans or standardized grading systems. Second, although tooth wear and tooth fracture were analysed in separate cohorts, we performed additional validation using an independent NHIS health screening database because access to the original dataset had expired. Our internal validation using an independent health screening cohort revealed that only 3.8% of patients exhibited both tooth wear and fracture simultaneously, and the temporal order of occurrence was heterogeneous. This low overlap supports our methodological decision to analyse these conditions in separate cohorts, as they represent distinct clinical entities with different trajectories (Supporting Information). Third, insomnia was defined using ICD‐10 diagnostic codes combined with a history of hypnotic prescriptions for at least 1 week. Although this approach enhances diagnostic validity, individuals with mild or untreated insomnia may have been missed. In addition, claims data do not provide sufficiently detailed information regarding dosage, adherence and duration of hypnotic medication use. Moreover, prior literature suggests that polypharmacy and medication switching are relatively common in the management of insomnia, with some patients experiencing drug additions or changes within months of treatment initiation, resulting in overlapping exposure periods [47, 48]. In routine clinical practice, hypnotic exposure is therefore often not limited to a single agent. While the independent contribution of specific hypnotics to the risk of tooth wear or fracture cannot be entirely excluded, the complexity and overlap of medication exposure in claims‐based data may constrain the ability to clearly distinguish drug‐specific effects. Future studies incorporating more refined exposure characterization or alternative study designs may help further clarify medication‐level influences. Fourth, as extensively described in the literature, tooth wear and fracture are influenced by a wide range of factors [49, 50]; however, many key mediators, such as salivary flow characteristics, dietary patterns, caffeine or stimulant consumption, oral hygiene habits and other dental or behavioural variables, cannot be directly measured in claims data. Moreover, trauma‐related codes do not specify the site of injury, and high‐prevalence conditions such as GERD cannot be uniformly excluded without risking substantial overexclusion and potential distortion of findings. Therefore, it was not possible to account for all relevant determinants of tooth wear and fracture, and unmeasured confounding remains a concern. Finally, this study was conducted using a Korean cohort, where stigma surrounding mental health may contribute to underreporting or avoidance of care. Thus, insomnia and related psychiatric conditions may be underrepresented in claims data [51]. Bruxism, classified under F‐codes, was excluded during cohort construction but may not have been completely eliminated. Moreover, OSA was not covered under Korea's health insurance benefits until 2018; it could not be reliably identified or excluded, raising the possibility that individuals with uncoded or undiagnosed OSA remained in the cohort. Additional psychological and sleep‐related factors may also remain unmeasured confounders, limiting generalizability to other populations.
Despite these limitations, this study provides meaningful epidemiological evidence suggesting a significant association between clinically diagnosed insomnia and the risk of tooth wear and fracture.
5. Conclusion
In this nationwide population‐based cohort study, clinically diagnosed insomnia was associated with a modest but consistently increased risk of both tooth wear and fractures. These associations remained robust across crude, adjusted and PSM analyses, although the observational nature of the data precluded causal inference. While several biological and behavioural pathways—such as nocturnal reflux vulnerability, salivary alterations, systemic inflammation and stress‐related neuromuscular changes—may offer plausible explanations, the current findings should be interpreted as associative rather than mechanistic.
Given the irreversible and progressive nature of tooth wear and fractures, early recognition of insomnia and its potential oral manifestations is crucial. Although the mechanisms linking insomnia to structural tooth damage remain hypothetical, dental professionals should be aware that signs such as unexplained enamel loss, occlusal wear or vertical fractures—particularly in older adults or patients with systemic comorbidities—may warrant consideration of underlying sleep disturbances. Multidisciplinary collaboration among dentistry, sleep medicine and primary care can facilitate the timely identification of sleep‐related factors and support integrated preventive and therapeutic strategies. Future prospective studies incorporating objective sleep assessments, standardized dental evaluations and behavioural data are needed to clarify the causal pathways and better inform prevention in at‐risk populations.
Funding
This research was supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (RS‐2025‐02223691). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (Nos. RS‐2024‐00339519, RS‐2026‐25494833).
Conflicts of Interest
The authors declare no conflicts of interest.
Supporting information
Table S1‐1: Cox regression results regarding tooth wear (Crude model): before and after propensity score matching.
Table S1‐2: Cox regression results regarding tooth wear (Adjusted model): before and after propensity score matching.
Table S2‐1: Cox regression results regarding tooth fracture (Crude model): before and after propensity score matching.
Table S2‐2: Cox regression results regarding tooth fracture (Adjusted model): before and after propensity score matching.
Table S3‐1: Association between insomnia and risk of tooth wear and fracture, stratified by hypnotic treatment status.
Table S3‐2: Association between hypnotic treatment and risk of tooth wear and fracture among individuals with insomnia.
Data S1: Supporting Information.
Acknowledgements
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS‐2024‐00339519, RS‐2026‐25494833); Korean Health Technology R&D Project (RS‐2025‐02223691).
Data Availability Statement
The data used in this study are available from the National Health Insurance Service (NHIS) database; however, restrictions apply to the public availability of the data used under the licence for this study. Access to the NHIS database for research purposes is granted through official approval, and de‐identified data are available upon request from a government agency via the National Health Insurance Sharing Service homepage (http://nhiss.nhis.or.kr).
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Table S1‐1: Cox regression results regarding tooth wear (Crude model): before and after propensity score matching.
Table S1‐2: Cox regression results regarding tooth wear (Adjusted model): before and after propensity score matching.
Table S2‐1: Cox regression results regarding tooth fracture (Crude model): before and after propensity score matching.
Table S2‐2: Cox regression results regarding tooth fracture (Adjusted model): before and after propensity score matching.
Table S3‐1: Association between insomnia and risk of tooth wear and fracture, stratified by hypnotic treatment status.
Table S3‐2: Association between hypnotic treatment and risk of tooth wear and fracture among individuals with insomnia.
Data S1: Supporting Information.
Data Availability Statement
The data used in this study are available from the National Health Insurance Service (NHIS) database; however, restrictions apply to the public availability of the data used under the licence for this study. Access to the NHIS database for research purposes is granted through official approval, and de‐identified data are available upon request from a government agency via the National Health Insurance Sharing Service homepage (http://nhiss.nhis.or.kr).