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BMC Oral Health
BMC Oral Health
BMC Oral Health
1472-6831
BioMed Central London

4832
10.1186/s12903-024-04832-3
Research
A nomogram for predicting the risk of temporomandibular disorders in university students
Cui Yuchen
Kang Fujia
Li Xinpeng
Shi Xinning
Zhu Xianchun zhuxc@jlu.edu.cn

grid.64924.3d 0000 0004 1760 5735 Department of Orthodontic, Hospital of Stomatology, Jilin University, No. 2699 Qianjin Street, Changchun, Jilin Province 130012 P.R. China
6 9 2024
6 9 2024
2024
24 104725 6 2024
29 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Objectives

Temporomandibular disorders (TMDs) have a relatively high prevalence among university students. This study aimed to identify independent risk factors for TMD in university students and develop an effective risk prediction model.

Methods

This study included 1,122 university students from four universities in Changchun City, Jilin Province, as subjects. Predictive factors were screened by using the least absolute shrinkage and selection operator (LASSO) regression and the machine learning Boruta algorithm in the training cohort. A multifactorial logistic regression analysis was used to construct a TMD risk prediction model. Internal validation of the model was conducted via bootstrap resampling, and an external validation cohort comprised 205 university students undergoing oral examinations at the Stomatological Hospital of Jilin University.

Results

The prevalence of TMD among university students was 44.30%. Ten predictive factors were included in the model, comprising gender, facial cold stimulation, unilateral chewing, biting hard or resilient foods, clenching teeth, grinding teeth, excessive mouth opening, malocclusion, stress, and anxiety. The model demonstrated good predictive ability with area under the receiver operating characteristic curve (AUC) values of 0.853, 0.838, and 0.821 in the training cohort, internal validation cohort, and external validation cohort, respectively. The calibration curves demonstrated that the predicted results were consistent with the actual results, and the decision curve analysis (DCA) indicated the model's high clinical utility.

Conclusions

An online nomogram of TMD in university students with good predictive performance was constructed, which can effectively predict the risk of TMD in university students. The model provides a useful tool for the early identification and treatment of TMDs in university students, helping clinicians to predict the probability of TMDs in each patient, thus providing more personalized and accurate treatment decisions for patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12903-024-04832-3.

Keywords

Temporomandibular disorders
Risk factors
LASSO regression
Boruta algorithm
Nomogram
issue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2024
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pmcIntroduction

Temporomandibular disorders (TMDs) are a group of painful and/or dysfunctional disorders associated with the masticatory muscles, temporomandibular joints (TMJs), and related structures [1], and are a growing public health problem. The main clinical symptoms include pain in the TMJ area and/or masticatory muscles, joint clicking and noises, and abnormalities or restrictions in mandibular movements. They may also involve headaches, ear symptoms, and neck and shoulder pain, among other effects [2, 3], significantly impacting the patients' quality of life.

The aetiology of TMD is complex, and its pathogenesis is not yet fully elucidated and is attributed to various factors. Current research indicates that risk factors include psychosocial aspects, occlusal factors, immunological factors, excessive joint load, anatomical factors of the joint, and other factors. Studies have shown that demographic characteristics such as gender and age are closely related to the occurrence of TMD [4]. Additionally, occlusal factors, including malocclusion, missing posterior teeth, and wisdom tooth problems, significantly impact occlusal stability and masticatory muscle function [5–7]. Specific oral habits, such as grinding teeth, clenching teeth, biting soft tissues (lips, tongue, cheeks), unilateral chewing, biting hard objects, excessive mouth opening, mouth breathing, and chewing gum, may lead to overloading of muscles and joints, which may trigger or exacerbate TMD symptoms [5, 8–10]. Behavioral habits, such as sleeping positions, resting the chin on the hand, poor neck posture, and prolonged use of mobile phones, may also indirectly contribute to the development of TMD by affecting joint or neck muscle tension [11–13]. Oral-related medical history and treatments, such as orthodontic, restorative treatments and facial cold stimulation, may also have an impact on the occurrence of TMD [5, 7, 14]. Psychosocial factors, such as anxiety, depression, and stress, are also key factors in the development of TMD [15].

TMD is most common in young and middle-aged adults between the ages of 20 and 40 years, with a high prevalence among university students. The prevalence of TMD reported in previous studies can vary due to differences in survey methodology and ethnic background, with the prevalence of TMD among university students reported in the literature ranging from 31 to 68% [3, 16, 17]. Given the potential impact of TMD on university students' learning, daily life, and oral health, and considering the high prevalence of TMD in this population, early risk assessment and intervention are of significant clinical and public health importance. Disease prediction models help to explore individualised traits that are closely associated with specific diseases and are increasingly used to support clinical decision-making. Despite some progress in TMD research, there has been little research on TMD risk prediction models, and there is a lack of validated TMD risk prediction tools specifically for the university population. Therefore, it is important to develop an effective TMD risk prediction model for university students by integrating the various potential factors leading to TMD, including biological, behavioral and psychosocial aspects.

The nomogram is widely used as a visualisation tool in the medical field. It is capable of quantitatively assessing the risk of a clinical event by considering multiple predictors simultaneously [18]. The use of nomograms allows clinicians to visualise the predicted risk of a disease and patients to understand their likelihood of developing the disease more clearly. This study aimed to identify the important risk factors for TMD in university students, and to construct and validate a nomogram model to predict the risk of TMD probability. This model will provide clinicians with a scientific and easy-to-use reference tool for risk assessment, aiding in the early identification of TMD patients and facilitating personalized treatment.

Materials and methods

Study population

From May to August 2023, a total of 1,231 university students were randomly selected through cluster sampling from four universities in Changchun City, Jilin Province. The inclusion criteria were students aged ≥ 18 years who agreed to participate in this study. Exclusion criteria included (1) individuals with systemic diseases; (2) individuals with tumours or craniofacial deformities; (3) individuals undergoing treatment with medications that could mask symptoms of TMD, such as nonsteroidal anti-inflammatory drugs or analgesics; and (4) individuals with a history of temporomandibular joint trauma or surgery. In addition, for external validation of the predictive model, we collected data from 205 university students undergoing oral examinations at the Stomatological Hospital of Jilin University between February and April 2024. This study was approved by the Ethics Committee of Jilin University (Approval number: JDKQ2023098) and was conducted according to the principles outlined in the Declaration of Helsinki. All of the participants were provided with information about the study and provided informed consent.

Data collection

A questionnaire survey was conducted for all of the participants, consisting of four parts: demographic and medical information, psychological state assessment, Fonseca Anamnestic Index (FAI), and Diagnostic Criteria for TMD (DC/TMD) Symptom Questionnaire. A total of 26 Influencing factors of TMD were included in this study. All questionnaires are included in Supplementary Questionnaires.

The first part was demographic information and related medical information. A questionnaire was designed to comprehensively collect influencing factors and demographic data related to TMD. This questionnaire included 6 dimensions and 24 items. The content of the questionnaire was as follows: (1) Demographic characteristics, including age, gender; (2) Oral-related medical history, including orthodontics, root canal or restorative treatment, facial cold stimulation; (3) Occlusal factors, including malocclusion, missing posterior teeth, impacted or displaced wisdom teeth; (4) Oral habits, including unilateral chewing, biting hard or resilient foods, chewing gum, biting of soft tissues (lips, tongue, cheeks), grinding teeth, clenching teeth, excessive mouth opening, mouth breathing; (5) Behavioral habits, including lateral sleep position, sleeping on the stomach, poor neck posture, resting chin on the hand, staying up late, prolonged mobile phone use; (6) Other factors, including insomnia, stress.

The second part consisted of the anxiety scale and depression questionnaire, which were used to assess the psychological states of the participants. The GAD-7 is used for assessing the patient's anxiety level [19]. It includes 7 items and uses a four-level scoring system, ranging from 0 points (not at all) to 3 points (nearly every day). The anxiety levels are classified into four categories: no anxiety (0–4 points), mild anxiety (5–9 points), moderate anxiety (10–14 points), and severe anxiety (15–21 points). The PHQ-9 is used for assessing the patient's depressive symptoms [20]. This questionnaire contains 9 items that are scored on a four-point scale from 0 points (not at all) to 3 points (nearly every day). The levels of depression are divided into five categories: no depression (0–4 points), mild depression (5–9 points), moderate depression (10–14 points), moderately severe depression (15–19 points), and severe depression (20–27 points). The Chinese version of the Generalized Anxiety Disorder-7 (GAD-7) and Patient Health Questionnaire-9 (PHQ-9) have been widely used in medical institutions and have good reliability and validity [21].

The third part was the FAI, which is used to identify the presence of TMD and to classify its severity [22]. The FAI has been translated into multiple languages and has been applied in different cultures and populations, demonstrating its validity and reliability [23, 24]. The Chinese version of the FAI was compared with the DC/TMD Axis I diagnosis and showed acceptable reliability and good validity [22]. This instrument is widely used in clinical and epidemiological research due to its high diagnostic accuracy, ease of use, and ability to quickly and effectively obtain epidemiological data [3, 16, 17, 25, 26]. It comprises 10 items that assess pain-related (headache, TMJ, and muscle and neck pain) and function-related (TMJ sounds, difficulties in opening, and lateral movements) TMD symptoms, as well as TMD risk factors (grinding teeth, malocclusion, and emotional stress). The scoring is performed on a three-level scale: 10 points (yes), 5 points (sometimes), and 0 points (no). The severity of TMD is classified into four categories: no TMD (0–15 points), mild TMD (20–40 points), moderate TMD (45–65 points), and severe TMD (70–100 points).

The fourth part was the DC/TMD Symptom Questionnaire (SQ), a standardized diagnostic instrument released by the International Academy of Dental Research (IADR) in 2014 [27], which contains objective questions about the symptoms of TMD. As an important tool for assessing TMD, the SQ is widely used in clinical practice and scientific research with good feasibility [28, 29]. The SQ contains 14 items covering the history of TMD and its main symptoms (facial pain, headache, TMJ sounds, limitations in opening the mouth, and limitations in closing the mouth, among other symptoms).

Two clinicians with standardized training were responsible for collecting and processing the results of the questionnaire, with inter-examiner and intra-examiner Kappa coefficients of 0.90 and 0.95, respectively. The results were assessed using uniform assessment criteria. Participants with FAI scores greater than 15 and DC/TMD Symptom Questionnaire results that met any one or more of the DC/TMD criteria for a subcategory diagnosis were categorised in the TMD group, and the remaining participants were categorised in the non-TMD group.

Model development and evaluation

A total of 26 predictors were included in this study. To identify the most important predictors, we employed various analytical methods. In the training cohort, univariate analysis was used to exclude predictors that did not show statistically significant differences. Subsequently, LASSO regression and the Boruta algorithm were applied to further select predictors that showed significance in the univariate analysis. LASSO regression optimizes variable selection by applying a penalty to the coefficients of less important predictors, while the Boruta algorithm can identify variables that significantly contribute to the model’s prediction. Then, independent risk factors were identified through multivariate logistic regression, and correlation analysis was used to explore the interrelationships among various predictors. Finally, a risk prediction nomogram model was constructed based on the selected predictors. The model's performance was assessed using receiver operating characteristic (ROC) curves, while calibration curves were used to evaluate the consistency between predicted and observed outcomes. Decision curve analysis (DCA) was utilized to determine the net benefit threshold for prediction, thus assessing the clinical effectiveness of the model. For ease of clinical application, a web-based interactive dynamic nomogram assessment tool was developed by using SHINY version 0.13.2.26.

Statistical analysis

Statistical analysis was performed by using SPSS version 25 (SPSS, Inc., Chicago, IL, USA) and R version 4.2.3 (R Foundation for Statistics Computing, Vienna, Austria). Categorical variables are expressed in numbers and percentages, and the chi-square test or Fisher’s exact test was used for comparisons between groups. Continuous variables that followed a normal distribution are expressed as the mean and standard deviation (SD), and the t-test was used for comparisons between groups; continuous variables that did not follow a normal distribution are expressed as the median (25th and 75th percentiles), and the Wilcoxon rank-sum test was used for comparing two groups. The significance level was set at p < 0.05. In this study, participants were randomly divided into a training cohort and an internal validation cohort in a 7:3 ratio. The external validation cohort consisted of 205 university students undergoing oral examinations at the Stomatological Hospital of Jilin University.

Results

Subject characteristics

The flow chart of the study is shown in Fig. 1. The study included 1,231 participants. Among them, 68 were excluded for not meeting the inclusion criteria and 41 were excluded because the questionnaire was incomplete or invalid. Finally, 1,122 participants who met the criteria were included in the study, including 831 (73.27%) females and 291 (26.71%) males, with a mean age of 19.99 ± 1.40 years, and a prevalence of TMD of 44.30%. These participants were randomized into a training cohort and an internal validation cohort in a ratio of 7:3. The external validation cohort consisted of 205 university students who underwent oral examinations at the Stomatological Hospital of Jilin University, and their inclusion met the established inclusion and exclusion criteria.Fig. 1 Flow chart of the study

The baseline characteristics of the training cohort and the internal validation cohort of the model are shown in Supplementary Table S1. The baseline characteristics showed that in the training cohort, all 25 factors were significant differences (P < 0.05), except for age, which was not significant differences for TMD (P > 0.05). In the internal validation cohort, all 23 factors were significant differences (P < 0.05), except for age, orthodontics, and missing posterior teeth, which were not significant differences for TMD (P > 0.05). The baseline characteristics of the external validation cohort are shown in Supplementary Table S2.

Selection of predictive factors

In the training cohort, we performed LASSO regression analysis based on the 25 factors that showed differences in the univariate analysis. Figure 2A displays the cross-validation error, and Fig. 2B shows the coefficient trajectories in the LASSO regression model, with coefficients as shown in Supplementary Table S3. The results indicated that when λ was 0.036 (one standard error of the minimum λ), only 14 predictive factors were retained in the model, and these were potentially the most influential for TMD. These predictive factors included gender, facial cold stimulation, root canal or restorative treatment, unilateral chewing, biting hard or resilient foods, clenching teeth, grinding teeth, excessive mouth opening, malocclusion, poor neck posture, resting chin on the hand, stress, anxiety, and depression.Fig. 2 Results of the LASSO regression analysis. A Tuning parameter (λ) selection cross-validation error curve. B LASSO Regression Coefficient Path Plot

Additionally, in the training cohort, based on 25 factors showing differences in univariate analysis, we used the Boruta algorithm for further selection. As shown in Fig. 3, 18 factors were identified as being potential predictors. They included gender, root canal or restorative treatment, facial cold stimulation, unilateral chewing, biting hard or resilient foods, biting of soft tissues, grinding teeth, excessive mouth opening, clenching teeth, malocclusion, sleeping on the stomach, poor neck posture, resting the chin on the hand, prolonged mobile phone use, insomnia, stress, anxiety, depression.Fig. 3 Boruta feature selection plot. Important is green, tentative is yellow, unimportant is red, shadow is blue

Based on the two analysis methods, 14 predictive factors were chosen by using a Venn diagram, as shown in Fig. 4. These factors included gender, facial cold stimulation, root canal or restorative treatment, unilateral chewing, biting hard or resilient foods, clenching teeth, grinding teeth, excessive mouth opening, malocclusion, poor neck posture, resting chin on the hand, stress, anxiety, and depression.Fig. 4 Venn diagram of the variables selected

In the training cohort, to clarify whether the abovementioned 14 variables were independent risk factors for TMD, a multivariate logistic analysis was conducted to further eliminate other confounding factors. The variance inflation factor for all of the variables was less than 10, thus indicating that they passed the multicollinearity test, as shown in Supplementary Table S4. The results demonstrated that except for root canal or restorative treatment, poor neck posture, and resting chin on the hand, which showed no statistical significance (P > 0.05), all of the remaining 11 factors had statistical significance (P < 0.05), as shown in Supplementary Table S5.

The correlation analysis of TMD risk factors is shown in Fig. 5. We found that anxiety and depression were highly correlated, with a correlation coefficient of 0.81, thus suggesting that they may share common underlying factors or can mutually influence each other. To simplify the model while retaining predictive power and combining clinical experience and related research, we decided to remove the factor of depression in the model construction, thus retaining the remaining 10 variables. The AUC values of these variables were all greater than 0.5, thus indicating that they have a certain predictive ability for TMD, as shown in Supplementary Fig. S1.Fig. 5 Correlation analysis of 11 predictors

Development of the nomogram model

The final logistic model included 10 predictive factors: gender, facial cold stimulation, unilateral chewing, biting hard or resilient foods, clenching teeth, grinding teeth, excessive mouth opening, malocclusion, stress, and anxiety. The odds ratios (ORs) and 95% confidence intervals (CIs) for these factors are presented in Fig. 6A. All of the ten variables conform to the variance inflation factor (VIF) standards in the ordinary least squares model (OLM), and their regression coefficients are listed in Supplementary Table S6. This model has been developed into a straightforward and easy-to-use nomogram, as shown in Fig. 6B, and is available online (https://nomodposlvj.shinyapps.io/dynnomapp-TMD/). In this nomogram, each variable was assigned a score on the point scale axis, and the total score could be easily calculated by adding each single score. By projecting the total score to the lower total point scale, we could assess the probability of TMD.Fig. 6 Construction of the TMD risk prediction model. A Forest plot of odds ratios (ORs) for predictors included in the prediction model. B Nomogram model for predicting TMD risk. To estimate the probability for an individual patient, the value of each factor is acquired on each variable axis; then, a line is drawn upwards to determine the point. The sum of these numbers is located on the total points axis, and a line is drawn downwards to the risk axis to determine the probability of TMD. For example, one of the females was characterized clinically by unilateral chewing, grinding teeth, malocclusion, and an anxiety score of 10 points. The subject had a total risk score of 116 points and a probability of developing TMD of approximately 79%

Performance and validation of the nomogram model

We used ROC curves, calibration curves, and DCA curves to evaluate the performance of the model. The results showed that the AUC values for the training, internal validation, and external validation cohorts were 0.853, 0.838, and 0.821, respectively, thus indicating that the model has good predictive ability, as shown in Fig. 7A-C. Internal validation and calibration of the model were performed by using the bootstrap method with 1,000 resamples. The results showed that the calibration curves of the three cohorts were basically consistent with the ideal curves, indicating that the prediction results were consistent with the actual results. Additionally, we evaluated the model's Brier score (ideal is 0, with values > 0.3 suggesting poor calibration) and calibration slope (ideal is 1) to further assess this consistency. The Brier scores for the training, internal validation, and external validation cohorts were 0.155, 0.166, and 0.176, respectively, and their calibration slopes were 1.000, 0.951, and 0.902, respectively, thus indicating high accuracy of the model, as shown in Fig. 7D-F. Furthermore, the DCA curves were used to determine the clinical utility of the prediction model by quantifying the net benefit across different threshold probabilities. The results demonstrated that the model provided higher net benefit across a wide range of thresholds, confirming its high practical value in clinical practice, as shown in Fig. 7G-I.Fig. 7 Performance and validation of the model. Performance and validation of the model. ROC curves of the model in the training cohort (A), internal validation cohort (B), and external validation cohort (C). Calibration curves of the model in the training cohort (D), internal validation cohort (E), and external validation cohort (F). Logistic calibration curves are indicated in red, and ideal reference lines are indicated in grey. The closer the calibration curve is to the ideal line, the higher the prediction accuracy of the nomogram. DCA curves of the model in the training cohort (G), internal validation cohort (H), and external validation cohort (I). The black and grey lines indicate the hypothetical conditions when no patients or all patients have TMD, respectively. The red line indicates the predictive performance of the actual model. A higher position of the red line indicates that the model has better clinical utility at that threshold

Discussion

In this study, we found that gender, facial cold stimulation, unilateral chewing, biting hard or resilient foods, clenching teeth, grinding teeth, excessive mouth opening, malocclusion, stress, and anxiety were significant risk factors for TMD in university students. Based on these factors, we constructed a TMD risk prediction nomogram model with AUC values of 0.838 and 0.821 for the internal and external validation cohorts, respectively, showing good predictive ability. Both calibration curves and DCA curves showed that the model has high accuracy and clinical utility. Additionally, we developed an online nomogram tool to predict the probability of TMD in university students, facilitating the model's application in clinical practice.

The TMD risk prediction model and online nomogram tool developed in this study effectively meet the clinical needs for predicting TMD in university students. This tool provides clinicians with a personalized and user-friendly assessment method, streamlining the evaluation process and facilitating early disease detection and timely intervention. Based on the prediction results, clinicians can develop more precise and effective preventive measures and treatment plans, which will help improve the overall management level of TMD and the quality of life of patients. Moreover, this study identified key risk factors for TMD in university students, providing a scientific basis for the development of public health strategies and helping to optimize the targeting of health education and preventive measures.

To enhance the model's performance, we employed the following strategies. First, LASSO regression was used for feature selection to eliminate the impact of multicollinearity and to improve model generalization. Second, the Boruta algorithm, which is a feature selection technique based on random forest, was utilized. It can consider the dependencies between features and evaluate the importance of each feature, thereby selecting more accurate and reliable features. The combination of LASSO regression with the Boruta algorithm resulted in an optimized set of features, thus enhancing the model's predictive accuracy and generalizability.

In this study, TMD was diagnosed in university students by using FAI combined with the DC/TMD Symptom Questionnaire. The results of the study showed that the prevalence of TMD among university students was 44.30%, which is a result that is close to the prevalence reported in other studies, such as Saudi students (46.8%) [17], Singaporean students (41.8%) [26], and Brazilian students (42%-68%) [3, 30].

Multiple studies have indicated that females are more prone to TMD than males [3, 16, 17, 26, 30–32]. Our study also supports this conclusion. This difference may be due to hormonal changes in women adversely affecting the TMJ [33]. Additionally, the shallower joint socket and the larger condyle in females increase the risk of joint instability. The analysis by Bueno and Gremillion [34, 35] found that the risk of TMD in women is twice that in men. A survey by Nomura et al. [36] of 218 participants demonstrated that among those with severe TMD, women were nine times more likely than men to experience this disorder. Moreover, women have a higher pain sensitivity than men [37] and are more inclined to seek medical attention when ill, thus contributing to the observed differences. TMD can occur at any age; however, it is more common in adolescents and young adults, with a higher proportion of middle-aged women. In our study, because the subjects were primarily university students with a relatively concentrated age distribution, there was no significant age-related difference in the occurrence of TMD.

The question of whether malocclusion affects the development of TMD has been a topic of debate. This study demonstrated that malocclusion is a risk factor for TMD, with certain specific occlusal issues impacting TMD. Research indicates that malocclusion alters the trajectory of condylar movement, and the TMJ needs to bear additional burdens and pressures, which may lead to inflammation, pain, and dysfunction of the TMJ [38]. Furthermore, abnormal occlusal positions and tooth wear can lead to tooth sensitivity, uneven bite force, and occlusal trauma, thus further exacerbating the load on the TMJ [39]. Studies by Abrahamsson et al. [40] have shown a higher prevalence of TMD in patients with dental malocclusions than in those without malocclusions. Henrikson et al. [41] found that TMD patients with malocclusions benefit from orthodontic treatment. Lai et al. [42]considered malocclusion as a cause of TMD. There have also been studies that focused on specific occlusal factors, such as posterior crossbite, anterior openbite, deep overbite, anterior tooth overlap, and tooth midline deviation, which may be associated with TMD [43–45]. However, some studies have indicated that most dental malocclusions are not significantly related to TMD [46, 47]. The relationship between malocclusion and TMD is a complex and important aspect, of which the static cusp-fossa interlocking relationship is only one part; in addition, and more importantly, the teeth exercise their function dynamically; thus, it is important to evaluate the occlusion based on dynamic situations.

Numerous studies have shown that grinding teeth and clenching teeth are related to the development of TMD [5, 48, 49], and this study also confirms that both are risk factors for TMD. Grinding teeth and clenching teeth increase the joint load, thus leading to adaptive changes in the TMJ structure, including disc displacement, joint capsule inflammation, and synovial oedema, which can affect joint function. It has been found that long-term grinding of teeth and clenching of teeth increase the tension in the masticatory muscles, thus leading to compensatory hypertrophy with a significantly greater thickness compared to the normal population [50]. These activities also cause muscle fatigue, thus leading to symptoms such as muscle pain, tension, and spasms, which can trigger symptoms of TMD. Additionally, grinding teeth and clenching teeth can exacerbate tooth wear, thereby affecting the occlusal relationship and increasing the risk of TMD. This study has also found that the risk of clenching teeth for TMD is slightly higher than that of grinding teeth. Clenching teeth reduces muscle blood flow, and the teeth are in an intercuspal position or maximum contact position, leading to isometric contraction of the elevating muscles (such as the temporalis and masseter), which are more prone to fatigue.

In this study, we found that unilateral chewing was significantly associated with TMD. Studies show that individuals who habitually chew on one side exhibit more signs and symptoms of TMD [51]. A study of Turkish university students found a significant association between unilateral chewing and TMD [52]. Long-term unilateral chewing may lead to changes in the internal stress of the TMJ, which may cause the imbalanced reconstruction of joint structures and alteration or even damage to the masticatory muscle fibres, resulting in uncoordinated bilateral muscle movements. Research by Heikkinen et al. [53] on 106 pairs of Lithuanian identical twins found that the volume of the jaw on the opposite side of habitual chewing increased, and this asymmetry could be a factor in the development and exacerbation of TMD symptoms. Diernberger et al. [54] also found that individuals who tend to chew on one side are more likely to experience TMJ pain and other TMD-related symptoms on the same side.

This study also suggests that biting hard or resilient foods is a risk factor for TMD. A survey by Akhter et al. [55] showed that the incidence of TMD in Bangladeshi adolescents is closely related to the intake of hard food, with the frequency and type of hard food consumption significantly correlated with the incidence of TMD. Studies by Paulino et al. [56] have also confirmed a significant correlation between TMD and biting hard objects. Chewing hard objects increases the load on the TMJ, which causes fatigue and spasms in the masticatory muscles, such as the temporalis and lateral pterygoid, and can also lead to tooth wear and malocclusion, all of which may increase the risk of TMD.

Studies have shown that the duration of continuous singing is positively correlated with the onset of TMD symptoms, with prolonged wide mouth opening being a risk factor for TMD [57]. Our study also found that excessive mouth opening was significantly associated with TMD and identified it as a risk factor. Excessive mouth opening can put the TMJ and its surrounding structures under high load for a long time. It may also displace the TMJ, thus causing problems such as joint strangulation, dislocation, or subluxation and thereby affecting the function of the TMJ. This suggests that iatrogenic strain during treatment, such as excessive and prolonged mouth opening, may also cause damage to the muscle ligaments and increase the risk of TMD.

This study demonstrated a strong correlation between facial cold stimulation and TMD. Among participants with TMD, 17.51% reported exposure to facial cold stimulation, compared to only 4.8% in those without TMD. Facial exposure to cold causes vasoconstriction, thus leading to reduced local blood flow, which may trigger or exacerbate joint pain and dysfunction. The study by Raghawan et al. [14] demonstrated that cold stimulation may promote or intensify inflammatory responses. Cold can also stimulate the synovium within the joint, which causes proliferative changes and aseptic inflammation, thus leading to fluid accumulation in the joint cavity, with patients experiencing symptoms such as joint pain, swelling, and restricted movement. Additionally, research indicated that cold stimulation can induce pain [58] and may also heighten the body's sensitivity to pain, consequently exacerbating individual responses to discomfort [59].

Psychological factors have a profound influence on the development of TMD. University students face various pressures and challenges, such as academic stress, interpersonal relationship troubles, and future employment concerns, which can lead to psychological issues such as anxiety and depression. Numerous studies have indicated a significant correlation between sociopsychological disorders (such as anxiety, depression, and stress) and the development of TMD [15, 25, 26, 56]. Slade et al. [60] reported that psychological states such as depression and stress are risk factors for the first onset of TMD in a three-year prospective cohort study, wherein there was an increased risk of developing TMD by 2–3 times. Another prospective cohort study confirmed that psychological state assessment could predict the occurrence of TMD [61]. Yap et al. [62] demonstrated that anxiety is a major psychological risk factor for TMD symptoms. This study indicates that anxiety, depression, and stress are all risk factors for TMD. In university students with TMD, over half of the students reported varying degrees of anxiety and depression, along with high levels of stress, with anxiety (66.40%), depression (66.20%), and stress (61.97%) being prevalent.

Currently, there is relatively little research on TMD risk prediction, especially regarding risk prediction models for university students. This study developed a nomogram model for predicting the risk of TMD in university students based on the identified risk factors for TMD, quantifying the likelihood of developing the condition. The innovation of this study is the inclusion of a wider range of risk factors such as demographic characteristics, occlusal factors, oral behavioral habits, lifestyle habits and psychological factors to provide a more comprehensive and accurate risk assessment. Secondly, the application of machine learning methods for factor selection effectively handles high-dimensional data and reduces overfitting. Thirdly, we not only conducted internal validation of the model but also performed external validation in an independent dataset, thereby enhancing its credibility and generalizability in practical applications. Finally, an online prediction tool was developed for clinical use, further enhancing the clinical utility of the research.

However, this study had certain limitations. First, the study sample primarily originated from universities in specific regions, which may limit its representativeness. External validation in diverse populations is crucial to confirm the generalizability of our findings. Second, the model may have considered only a limited set of variables; thus, further research is needed to integrate new predictive factors to improve the accuracy of the nomogram model. Additionally, the study may have relied on self-reported data, which can be influenced by subjective perceptions and may affect the accuracy of the data, thus potentially overestimating or underestimating the impact of certain conditions on the outcome. Future research could broaden the sample scope, thoroughly consider various potential predictive factors, enhance data quality, and further investigate the impact of these risk factors on different TMD subgroups.

Conclusion

This study identified 10 key predictors for TMD risk in university students, including gender, facial cold stimulation, unilateral chewing, biting hard or resilient foods, clenching teeth, grinding teeth, excessive mouth opening, malocclusion, stress, and anxiety. Based on these predictors, we have developed and validated a nomogram model with good predictive performance and high clinical utility. Additionally, we developed a simple and intuitive online nomogram tool for predicting the risk of TMD in university students. This tool not only provides clinicians with a convenient method to assess the likelihood of TMD but also supports early diagnosis and the development of personalized treatment plans for TMD patients.

Supplementary Information

Supplementary Material 1: Supplementary Table S1. Demographics and baseline characteristics in the development cohort. Supplementary Table S2. Demographics and baseline characteristics in the external validation cohort. Supplementary Table S3. The coefficients of Lasso regression analysis. Supplementary Table S4. Multicollinearity analysis. Supplementary Table S5. Results of multivariate logistic regression for the training cohort. Supplementary Table S6. Final results of multivariate logistic regression for training cohort. Supplementary Fig. S1. ROC curve analysis of 10 predictors. Supplementary Questionnaires. Demographic information and related medical information, GAD-7, PHQ-9, Fonseca Anamnestic Index Questionnaire, DC/TMD Symptom Questionnaire.

Acknowledgements

We would like to thank all the hospital colleagues who participated in the data collection for their long-term dedication and selfless help in the research project.

Authors’ contributions

Yuchen Cui: conceptualization, methodology, investigation, data curation, validation, writing – original draft. Fujia Kang: investigation, data curation, software, visualization. Xinpeng Li: methodology, investigation, software. Xinning Shi: investigation, software, validation. Xianchun Zhu: methodology, resources, writing – review & editing, supervi-sion, funding acquisition. All authors have read and approved the final manuscript.

Funding

This work was supported by the Natural Science Foundation of Jilin Province (YDZJ202201ZYTS057) and the Jilin Provincial Key Research and Development Plan Project (20210203064SF).

Availability of data and materials

Relevant data can be obtained by contacting the corresponding author.

Declarations

Ethics approval and consent to participate

The study was approved by the Ethics Committee of the Stomatology Hospital of Jilin University (Approval number: JDKQ2023098). All patients were informed and consented to participate in the study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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