Background
Dental caries is one of the most common chronic infectious diseases in children, and it is a major health problem in many countries. Early childhood caries (ECC) is defined as the presence of one or more decayed (non-cavitated or cavitated lesions), missing teeth (because of caries), or filled tooth surfaces in any primary tooth in a child under 72 months of age [
1,
2]. The negative impacts of ECC on children’s quality of life are numerous. ECC impair nutrition, speaking, occlusion, social behaviors, and the patient’s self-esteem. The prevalence of ECC in preschool children varies across countries; Furthermore, ECC is a common infectious disease in the majority of countries all over the world [
3].
Dental caries is a multifactorial disease that results from multiple factors, including immature immune systems, individual characteristics of saliva, cariogenic microorganisms, hyposalivation, enamel hypoplasia, enamel defects, cariogenic diet, night breastfeeding, and oral hygiene care in early childhood [
4]. Premature birth, low birth weight, and lack of access to nutritional supplements during pregnancy are other risk factors of ECC [
5]. Socioeconomic factors such as parent’s income and educational level, birth order, and dental insurance coverage are the other influential factors on caries risk [
6]. Recently salivary components and characteristics have been introduced as one of the crucial factors affecting ECC development [
7]. ECC treatment generally does not have a long-term impact on the population of oral
Streptococcus mutans. Although these treatments help control the disease, caries recurrence is still common around or after repair, with a recurrence rate of approximately 40% reported during the first year [
8,
9].
Saliva is a unique body fluid that is easily accessible and contains complex components with great diagnostic value. Saliva collection and storage are simple, non-invasive, and inexpensive. [
10]. The salivary immune system is composed of proteins with a significant effect on oral health. Many salivary proteins prevent the adhesion and aggregation of cariogenic bacteria, and some others have a role in defense against microorganisms [
11]. Qualitative and quantitative changes in salivary proteome have a significant role in the initiation and intensity of oral disease [
12]. Although protein components of saliva are only 30% of the blood components, it is still known as a rich source of biomarkers [
7]. Cystatins are a large family of proteins that control and reversibly inhibit the activity of extracellular cysteine proteinases in inflammatory conditions [
13]. Cystatin S is a phosphorylated protein that is found in the tear and saliva. It is mainly secreted by the submandibular gland and to a much lesser extent by the parotid and sublingual gland [
14]. Unlike other proteins in this group, cystatin S has a lower cysteine proteinase inhibitory function [
15].
Cystatin S has four sites for phosphorylation that bond with hydroxyapatite and has an important role in dental pellicle formation and calcium and phosphate equilibrium and cause enamel remineralization. It also prevents enamel demineralization by attachment to the enamel surface. Cystatin S protects the oral mucosa by its antimicrobial and antiviral effects [
16,
17].
Artificial intelligence (AI) is a computer's ability to perform reasoning processes that generally associate with intelligence in human beings. Machine learning is one of the most common forms of artificial intelligence that consists of a set of instructions for processing and finding patterns in large data sets to enable decision-making without human intervention [
18]. A large quantity of data in the healthcare field, from clinical symptoms to imaging features, requires machine learning algorithms for classification and regression tasks. As the result of applying machine learning algorithms to clinical data, patients and clinicians benefit in many different ways, such as clinical decision support and the development of clinical care guidelines [
19].
Considering the role of cystatin S in caries prevention, this study aims to compare the salivary level of cystatin S in ECC patients and caries-free (CF) children.
Methods
Ethical statement
This study was approved by the Tehran University of Medical Sciences Ethical Committee (ethical code: IR.TUMS.DENTISTRY.REC.1398.099) and this study was conducted in accordance with the Declaration of Helsinki [
20]. After describing the study objectives, written informed consent was obtained from a parent or guardian for participants under 16 years old. The authors confirm that all methods were performed in accordance with the relevant guidelines and regulations.
Samples
A cross-sectional case–control study was undertaken on 20 cases of ECC and 20 CF children as a control group. The participants enrolled in this study were selected from children referred to the dental clinic of Tehran University of Medical Sciences (Tehran, Iran) for routine oral examinations. The oral health status of each participant was determined by three professional dentists. The inclusion criteria were children aged between 48 and 72 months with one or more decayed (non-cavitated or cavitated lesions), missing teeth (because of caries), or filled tooth surfaces in any primary tooth. CF controls were matched regarding age and gender. Children with no clinical signs of early caries or white spots were considered to be free of caries and their Decayed, Missing, and Filled Teeth (dmft) index was zero.
After oral examination of participants, the researcher completed the checklist by interviewing parents, which collected information about the demographic characteristics of child and parents, dietary intakes [
21], birth weight, oral hygiene behaviors, parental education level, medicine intake, and night breastfeeding. Exclusion criteria include children who had chronic systemic diseases or syndromes, influenza or infection of the respiratory system, taking medicine and received antibiotic therapy within three months, fluoride prophylaxis within 1 year, and medical history of congenital diseases, parents refused to participate or refused to sign the informed consent and children who did not agree or cooperate with the participation. Children were assessed using dmft index based on WHO Oral Health Surveys Basic Methods [
22], and diagnosis of ECC was done based on the diagnostic criteria of the American Academy of Pediatric Dentistry [
23]. Those having a dmft index of zero were considered CF. After surveying and regarding the exclusion criteria, 20 children were finally selected as cases, and 20 of them were considered as control.
Saliva collection
Unstimulated whole saliva samples by the suction method were collected. Before sampling, the children were in a rest position and did not eat anything for 30 min. Saliva sampling was done between 9:00 am to 11:00 am to avoid circadian variations in the case and control group. Complete protease inhibitor cocktail (Roche Diagnostics GmbH, Mannheim, Germany) was added immediately after the completion of saliva collection. Saliva samples were centrifuged at 10,000 × g for 15 min at 4 °C; the supernatant was obtained and stored in − 80 °C.
Determination of salivary cystatin S levels
Cystatin S concentrations were determined using human cystatin enzyme-linked immunosorbent assay (ELISA) kit (ZellBio GmbH, Ulm, Germany). The samples were thawed at 25 °C and assayed in accordance with the manufacturer’s instructions. The absorbance of samples at 450 nm was measured using Hyperion ELISA microplate reader. The concentrations of cystatin S were determined by spectrometer software based on standard curves.
Statistical analysis
Statistical analysis was performed with SPSS software (version 22; SPSS Inc., Chicago, IL, USA) and GraphPad Prism 8.2.1 for Windows (GraphPad Software, San Diego, California). In order to assess the relationship between dental caries and age with cystatin S salivary level, T-test, and Levenes' test were used. Mean value and standard deviation of cystatin S levels were reported. Regression analyses were performed for cystatin S level with the backward stepwise method. The contribution of each variable (including ECC, demographic and clinical characteristics, and nutrition habits) was expressed by the p-value (p) and standardized coefficients beta (β). The receiver operating characteristic (ROC) curve analysis was done to evaluate the potential role of cystatin S salivary level and combination of cystatin S salivary level with weight of birth for early diagnosis of ECC. All results are presented as mean ± standard deviation (SD), and p ≤ 0.05 was considered statistically significant.
Machine learning analysis
We applied multiple supervised machine learning methods to assess the usefulness of cystatin S in addition to demographic and clinical factors, besides nutrition habits in predicting ECC. Supervised learning models are trained using labeled training data, where the model learns about each type of data. After the training phase is completed, the model is evaluated on the test data to predict the type of unlabeled data. In this paper, several supervised learning models such as feed-forward neural networks, XGBoost, Random Forest, and Support vector machine (SVM) are used to generalize our findings.
For implementing a feed-forward neural network (Additional file
1: Figures. S1 and S2), we utilized Python Software Foundation, Version 3.7, and the open-source deep learning package, namely Keras [
24], which is a high-level neural network API. The proposed feed-forward neural network model has two hidden layers, and each hidden layer has 32 neurons (Additional file
1: Figures S1, S2). We used the ReLU
1 activation function for the hidden layers, and for the output layer, the sigmoid activation function was used. We utilized the binary cross-entropy method as the loss function. In order to set the hyper-parameters, we used the fivefold cross-validation method, and the Adam optimizer [
25] technique was utilized to update the weights and bias parameters of the neural network layers. Additionally, we utilized the scikit-learn package
2 to construct the previously discussed supervised learning methods, including XGBoost, Random Forest, and Support vector machine, and ran all classifiers with default parameter settings. To assess the effectiveness of cystatin S levels in predicting ECC, for each supervised method, we construct two models:
-
The first model uses the clinical features, patient characteristics, and nutrition habits as the input features for ECC prediction.
-
The second model, in addition to the features used in the first model, utilizes cystatin S levels as the input feature to predict ECC.
The ROC curve analysis, in addition to the rate of accuracy, sensitivity, and specificity measures of two constructed models, are provided in the machine learning results section.
Discussion
ECC is premature dental caries in deciduous teeth that mainly affects smooth surfaces and is one of the most common chronic infectious diseases in children [
31]. ECC is a preventable disease, and its early diagnosis is critical [
32]. Besides diagnosed ECC risk factors, changing salivary composition is one of the risk factors with significant importance in early disease diagnosis [
33]. Studies have shown that some salivary compounds have different levels of biological activity in active caries individuals in comparison with CF ones. However, there are limited studies on the difference between salivary protein components in ECC and CF children. Cystatin S is a cystatin protease inhibitor, which is mainly present in submandibular saliva. The phosphorylated form of cystatin S has a significant role in the regulation of salivary calcium levels [
34,
35]. Our study assessed salivary cystatin S levels by ELISA revealed that mean value of salivary cystatin S levels was significantly higher in CF children than ECC group (p < 0.005). Furthermore, the sensitivity of cystatin S in caries diagnosis was 95%, and its specificity was 65%. Considering the AUC [
36,
37], it could be concluded that cystatin S may be a proper salivary biomarker for early diagnosis of ECC risk.
These results are in line with Vitrino et al. study. They assessed salivary proteins involved in dental pellicle formation. They concluded that the level of cystatin S in dental pellicle in CF individuals was higher than dental caries ones. There was a direct relationship between levels of salivary cystatin S and lower values of dmft [
38]. Wang et al., in a study with the aim of salivary proteomics investigation in children with and without caries in the age range of 10–12 years, concluded that the levels of salivary cystatin S in the group without caries is significantly higher than the group with high dental caries [
39]. A study by Siqueira et al., compares the protein composition of dental pellicle in patients with and without dental caries. They revealed that cystatin S level in CF group was higher than the dental caries group [
40].
Odanaka et al. revealed that cystatin S in dental pellicle originated from saliva [
41]. Age is one of the important factors that cause significant changes in saliva composition and plays an important role in determining the proteins of healthy and cariogenic saliva. Cabras et al. stated that the level of salivary cystatin S changes at different ages and their results indicated that the salivary cystatin level increases with aging [
42]. Assessment of salivary proteome in the first 48 months of life revealed that cystatin isn’t present in saliva in the first 6 months. It can be concluded that salivary cystatin concentration is age-related and an increase in age and physiologic condition have a great impact on it [
43].
The level of cystatin S in the elderly differs from childhood. Preza et al., in a comparison between elderly patients with root caries and elderly patients without root caries as the control group, concluded that the level of parotid cystatin S in elderly patients with root caries was significantly higher than the control group [
44]. These results are contrary to the results of our study, which can be attributed to the role of age on the concentration of salivary cystatin S. Regarding the relatively higher expression of salivary cystatin S in CF group, we anticipate a significant relationship between expression of salivary cystatin S and caries risk factors. Considering the high level of cystatin S in CF children, a statistically significant relationship between cystatin S and caries risk factors is anticipated. Julihn suggested that birth order is associated with caries increment in young children [
45]. Compared with first-born children, the highest risk of caries increment occurred in fifth- or later-born [
45‐
47]. In our study, there was a statistically significant inverse relationship between expression of salivary cystatin S and birth order. The level of salivary cystatin S in first-born children of the family was higher. These results are consistent with the study by Julihn, which stated that second-, third-,fourth-, and fifth- or later-born children have a significantly increased risk of developing new caries lesions between age 3 to 7 years compared with first-born children [
45]. Increasing caries risk by birth order may be attributed to lower attention of parents to oral hygiene habits of children later born. In addition, according to our results, the level of cystatin S in the ECC group is lower than the control group. It could be concluded that by increasing birth order, salivary cystatin s levels decreased.
Another risk factor of dental caries is low birth weight. Some studies demonstrated a significant relationship between dental caries and preterm low birth weight status. Bernabé et al. concluded that low birth weight and maternal smoking cause an increase in caries rate. The results were in line with ours. Nicolau et al. in a study concluded that low birth weight children, which were second-born child in the family, had a higher dmft index [
48]. Hallett et al. in their study reported that prevalence of ECC in fourth-born children of the family was significantly higher (p = 0.001) [
49].
These differences between siblings can be for a variety of reasons, including the time parents spend with children or changes in the family as a result of the presence of children of different ages. The first child usually gets all the attention of the parents, at least in the first period of life. Although younger children can benefit from the education of their older siblings, this is a mutual benefit, and it has even been said that older children benefit more from teaching their younger siblings. It is also not hard to imagine that many things that children learn from their older siblings may have a negative effect [
50].
Birth problems, such as low birth weight, can lead to decreased immune function or enamel hypoplasia and early establishment of cariogenic bacteria in the oral cavity, which increases the risk of dental caries [
44]. In our study there was a statistically significant relationship between birth weight and salivary levels of cystatin S which was similar to Rajshekar et al.’s study. They concluded that prevalence of dental caries has a significant relationship with birth weight and low birth weight children had higher dental caries than normal weight children [
51].
Bernabé et al. also found that maternal weight and maternal smoking were associated with changes in dmft. Children with low birth weight and smoking mothers showed a higher rate of caries than children with normal birth weight and non-smoking mothers [
52]. These results were in line with ours but Athamneh et al. assessed 30–48 months children and concluded that there isn’t a significant relationship between birth weight and dental caries [
53].
Some studies have shown that the children of parents with higher education have better oral health. A statistically significant positive correlation between salivary cystatin S levels and parent education was found in ours. Highly educated families have a better economic situation, which leads to more benefits from health services and improving their children's oral health.
Another aspect of the impact of parental education is increasing parental awareness of oral hygiene and caries prevention [
54]. The present study did not show a significant relationship between salivary cystatin S levels and oral health and nutritional variables. Based on the results of other studies, frequency of sugary snacks and sugary drink intake significantly affect dental caries in children. Also, there was a higher caries risk in children who often snacked before sleep than those who never snacked before sleep. In this study, the frequency of eating sweet snacks in ECC group was slightly higher than control group but this difference was not statistically significant that may be attributed to small sample size. Regarding oral hygiene habits frequency of toothbrushes and dental flossing in ECC group was lower than control. These results are in line with the other studies, which state that brush at an earlier age and parent-assisted brushing can often reduce the risk of caries [
55]. Given that these variables are all part of the risk factors for dental caries and various studies have found a significant relationship between them and dental caries, the result of our study can be due to parents' dishonesty in responding to questions about oral health and nutritional habits because parents tend to show their child's health and eating habits better than they already are.
In addition to studying demographic information, the relationship between demographic data and oral habits with the salivary cystatin S levels was investigated in ECC children in comparison with CF cones. Applying the information mentioned above to a set of machine learning methods confirmed our achieved findings. Using machine learning methods in oral healthcare improves dentist checkup skills and introduces novel and complex cause-and-effect relationships, which are not easily possible by examining and receiving a patient’s history.
Machine learning methods do not cause easily identify crucial factors of diagnosing ECC levels but help us develop computer algorithms that can consider a set of variables and their complicated relationships. Although these complex relationships are challenging for human beings, with the help of machine learning methods, they are comprehensible. By taking advantage of machine learning in clinical issues, many useful facilities in public health are provided. Machine learning can be used as a screening tool in dentistry.
This study was a step towards better understanding the early diagnosis of ECC. One limitation of this study is the sample size. Since this study was performed during the Covid-19 pandemic, we had to reduce the sample size to the minimum acceptable number based on the the amount determined using the sample size equation. There were other limitations, including the use of self-reporting measures for all variables. Although the questioner provided sufficient and convincing explanations to the parents, there was a mistake that the parents were trying to make the child's living conditions better or different from the real ones. Considering the presented limitations, caution is necessary for interpreting this analysis and generalizing it to other groups of children. However, it is suggested to provide more comprehensive models by increasing the sample size and following up in different salivary protein markers and demographic data periods.
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