Dietary Patterns and Severity of Symptom with the Risk of Esophageal Squamous Cell Carcinoma and its Histological Precursor Lesions in China: A Multicenter Cross-Sectional Latent Class Analysis

Dietary Patterns and Severity of Symptom with the Risk of Esophageal Squamous Cell Carcinoma and its Histological Precursor Lesions in China: A Multicenter Cross-Sectional Latent Class Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Dietary Patterns and Severity of Symptom with the Risk of Esophageal Squamous Cell Carcinoma and its Histological Precursor Lesions in China: A Multicenter Cross-Sectional Latent Class Analysis Zhaoping Zang, Yong Liu, Jialin Wang, Yuqin Liu, Shaokai Zhang, and 22 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1038856/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background: Dietary patterns and symptoms research among Chinese with esophageal squamous cell carcinoma (ESCC) and its precursor lesions is limited, especially as it relates to multiple food consumption and multiple co-occurring symptoms. The aim of our study was to identify the dietary patterns and severity of symptom classes with the risk of esophageal squamous cell carcinoma and its histological precursor lesions, and develop a risk prediction model for different stages of esophageal disease. Methods: We analyzed data from a multicenter cross-sectional study carried out in ESCC high incidence areas between 2017 and 2018, which included 34,707 individuals aged 40-69 years. Dietary patterns and severity of symptom classes were derived by applying a latent class analysis (LCA). A multiple logistic regression model was used to derive the odds ratio (ORs) and corresponding 95% confidence intervals (CIs) for ESCC and the different stages of esophageal disease according to the dietary patterns and severity of symptom classes identified. We built the risk prediction model by using a nomogram. Results: We identified five dietary patterns and three severity of symptom classes. The dietary patterns were classified as follows: “Healthy”, “Western”, “Lower consumers-combination”, “Medium consumers-combination” and “Higher consumers-combination” patterns based on the intake of foods such as red meat, vegetables and fruits. The severity of symptoms was categorized into “Asymptomatic”, “Mild symptoms” and “Overt symptoms” classes based on health-related symptoms reported by the participants. Compared to the “Healthy” pattern, the other four patterns were all associated with an increased risk of esophageal disease. Similarly, the other two symptom classes present different degrees of increased risk of esophageal disease compared to the “Asymptomatic”. The nomograms reflect the good predictive ability of the model. Conclusion: Among individuals aged 40-69 years in high incidence regions of upper gastrointestinal cancer, the results supplied important epidemiological evidence and given further insights into dietary patterns and symptoms research. Cancer Biology Esophageal squamous cell carcinoma Precancerous lesions Latent class analysis Dietary patterns Symptom Figures Figure 1 Figure 2 Figure 3 Background Esophageal cancer ranks seventh in terms of incidence and sixth in mortality overall in the world according to the report of the Global Cancer Statistics 2020. Eastern Asia shows the highest regional incidence rates for both men and women, partly because of the large burden in China [ 1 ]. In China, esophageal cancer is the sixth most common malignancy and the fourth leading cause of cancer-related deaths [ 2 ]. Esophageal adenocarcinoma (EADC) and esophageal squamous cell carcinoma (ESCC) are the two most common histologic subtypes of esophageal cancer. In China, more than 90% of esophageal cancer cases are ESCCs, whose number accounts for about half of all the ESCC cases on earth [ 3 ]. The most surveyed region of China is the North Central Taihung Mountain range. In small areas of this region, ESCC may be at or nearly the leading cause of death, with incidence rates exceeding 125/100,000 per year [ 4 ]. Therefore, identification of risk factors in the early stages of the disease appears to be essential in order to decrease ESCC incidence and mortality. Esophagitis is a precancerous disease of esophageal cancer, and it is a benign disease with a certain canceration rate. Based on WHO tumor histological classification, the esophageal precancerous lesions (EPL) can be defined as low-grade intraepithelial neoplasia (LGIN) and high-grade intraepithelial neoplasia (HGIN) [ 5 ]. With the aggravation of precancerous lesions, the rate of developing esophageal cancer increased from 24–74% [ 6 ]. Therefore, intervention at an early stage of the disease results in a significant decrease in ESCC incidence and mortality. The etiology of ESCC is multi-factorial and strongly population dependent. A study estimated a population-attributable risk of 89% using only cigarette smoking, alcoholic beverage consumption, and low consumption of fruits and vegetables [ 7 ]. There is also some evidence on the protective effect of fruit and vegetable and the potential harmful effect of processed/red meat consumption. The evidence on the effect of diet on ESCC risk is however still suggestive or limited [ 8 ]. Before a diagnosis of esophageal cancer is established, majority of the patients have experienced pain, dysphagia, eating difficulties, appetite loss, bloating and nausea resulting in patients’ daily living and quality of life [ 9 ]. Therefore, it is more meaningful for the prevention of esophageal cancer that identifying the high risk dietary patterns and some classical symptoms in the early stage of esophageal cancer. The analysis of dietary patterns aims to fully explore the complexity of the diet, as an alternative to the study of isolated components. These techniques depend upon the concept that food consumption can be effectively presented by reproducible patterns, in spite of individual variations, and that food eaten together may have interactive effects on the risk of cancer. Similarly, the symptoms are the same. Identifying symptom clusters and their relationship to patient characteristics may lead to a better interpretation for identifying patients with early lesions and provide greater insight into the planning of future interventions. Latent class analysis (LCA) is a model-based cluster analysis technique that allows for identifying prevalent, mutually exclusive, eating behaviors and symptoms with additional advantages with respect to the classical approaches [ 10 ]. Unlike principal component analysis (PCA) and factor analysis (FA), it can be used to categorize individuals into mutually exclusive groups, dietary patterns or severity of symptoms and differently from cluster analysis, it grants quantification of the uncertainty of class membership, and assessment of goodness of fit [ 11 ]. Our study aims to identify dietary patterns and severity of symptom through LCA, and thus to model screening for different stages of the disease. Therefore, adding a new perspective on the association between dietary habits and symptoms and ESCC in China. Methods Study population This was a multicenter cross-sectional study, depending on high incidence regions of esophageal and gastric cancer established by the cancer early diagnosis and early treatment project in China [12]. In 2017, a new screening study of upper gastrointestinal cancer in five high-risk regions of upper gastrointestinal cancer in China, including Hebei, Henan, Shandong, Shanxi, and Gansu Provinces was released. The main purpose of this project was to confirm the high-risk population of malignant upper gastrointestinal cancer and to establish a cancer risk prediction model to provide support for the prevention of upper gastrointestinal cancer. The inclusion criteria were as follows: (1) local permanent residents in selected regions, (2) no history of endoscopic examination during the last 3 years, (3) no history of cancer, mental disorder, or any contraindication for endoscopy, (4) signed informed consent and (5) agreement to complete the entire survey and examination, including endoscopy. The participant selection process is shown in Figure 1. We recruited participants from April 2017 to December 2018. The final analysis included 34,707 residents aged 40-69 years. Among these participants, there were 81 persons with ESCC, 251 persons with HGIN, 1,413 persons with LGIN, 3,883 persons with esophagitis and 29,079 persons serving as normal esophagus controls. The study was approved by the Capital Medical University, Chinese Academy of Medical Sciences and Peking Union Medical College. The experimental protocol involving humans was in accordance to the guidelines of the Declaration of Helsinki. Diet and symptoms assessment Comprehensive questionnaire information was collected by face-to-face interviews and entered directly into a laptop based data entry system by trained investigators. The data entry process was conducted with software designed to decrease missing items and reduce logic inaccuracy. A questionnaire typically took 35-45 minutes to complete. Items of dietary intake were selected through the above questionnaire, including livestock meat, poultry meat, seafood, eggs and their products, vegetables, fruits, bean products, scallions, ginger and garlic, pickles and nuts. All the variables were categorical. Foods that are consumed more frequently, were divided into three categories namely: every day, 1-6 days per week, and less than 1 day per week. Foods that are consumed infrequently, were divided into two categories namely: at least one day per week and less than one day per week. Items of typical symptoms were also selected through the above questionnaire, including number of lost teeth, frequent bleeding of gums, dysphagia, bloating, heartburn, acid reflux, nausea, vomiting, belching and epigastric pain. The number of lost teeth were categorized into three groups namely: never dropped, 1-3 teeth and more than 4 teeth. Other variables are categorized as yes or no. Outcome assessment The endoscopic examinations were carried out by physicians at local hospitals. Procedures were based on clinical guidelines for cancer screening and early diagnosis and treatment in China. Lugol’s iodine staining method was used to identify suspicious tissues, which were then biopsied. To confirm severity, the esophageal mucosa was ranked into 5 categories: normal esophageal mucosa, minor mucosa changes, esophagitis, esophageal squamous simple hyperplasia (ESSH) or esophageal squamous dysplasia (ESD) [13]. ESD was further classified into 3 levels including slight, moderate, and severe. According to WHO tumor histological classification, mild and moderate ESD combined fall under LGIN. Severe ESD and squamous cell carcinoma in situ are considered as HGIN. If there were any inconsistencies, a third pathologist would give advice through discussion. Doctors reported the worst biopsy diagnosis to be from participants with multiple lesions. In this study, we divided the participants into 4 groups: normal control, esophagitis, LGIN and HGIN/ESCC. Statistical methods We characterized the dietary patterns and symptom patterns, assumed as unobserved mutually exclusive, with different variables probability distributions, by performing LCA on the observed responses on the different items. LCA identified latent classes of participants based on the ten dietary variables and six symptom variables. Estimation was conducted with the robust maximum-likelihood and expectation-maximization algorithms [14]. Statistical fit indexes were used to assess model fit and to decide the final number of latent classes. The model that fits the data best was selected by a combination of the following criteria: (a) the lowest Akaike information criterion (AIC), (b) the lowest Bayesian information criterion (BIC), (c) the lowest Lo–Mendell–Rubin likelihood ratio test (LMR), (d) the lowest Lo–Mendell–Rubin Adjusted LMR test (ALMR), and (e) entropy to be 0.6 or greater [15]. Next, we executed an unconditional multivariable logistic regression to identify sociodemographic and risk factors that predicted class membership. Before conducting the analysis, we performed a covariance diagnosis between the independent variables. We considered models to calculate the adjusted odds ratios (ORs) and 95% confidence intervals (CIs), including age, gender, education, body mass index (BMI), smoking and drinking at the same time. We also used a nomogram to model normal controls separately from the different stages of the disease. Evaluation of the model was performed using calibration curves and decision curves. In addition, basic, descriptive statistics show categorical variables as percentages, while continuous variables are shown as mean and standard deviations. LCA was conducted in both cases and normal controls. Analysis of only the normal control was performed to check the robustness of the previous solution. As dietary patterns and symptom severity classes identified on controls were consistent (number and characteristics of the patterns) with the ones obtained on the overall dataset, we based all our analysis on the overall dataset. To guarantee the internal reproducibility of the chosen solution the analysis was conducted separately in two randomly selected subsets of the original data several times. Statistical analyses were performed using Mplus (version 8.1) and R (version 3.6.3) software. All tests were two-sided and had a significance level of 0.05. Results When fitting the LCA model, we selected the result with 5 classes for dietary patterns and 3 classes for the severity of symptom in every group according to the criteria in methods (Table 1 and 2). Latent classes of dietary patterns Table 3 shows the conditional distribution of food group intake for all participants, giving the latent classes for the food groups that were more relevant in discriminating and labeling the clusters. The table of the normal control and the other three groups of different stages of disease are given in the Supporting Information Table S1-S3. We identified five classes with similar probability distributions according to a previous study [10], both for all participants and for different stages of the disease. Cluster 1 labeled “Healthy pattern”, showed higher probability to consume more fruiting vegetables and all other kinds of fruits, high-quality protein and lower probability to consume red meat. Subjects in Cluster 2, “Western pattern,” reported higher consumption of red meat and lower consumption of vegetables and fruits. Clusters 3 to 5 were related to previous food groups, but with a difference in the amount of intake. We termed Cluster 3 “Lower consumers-combination pattern” as people in it were less likely to eat all kinds of food. Cluster 4 had slightly higher probability than cluster 3 to eat all kinds of food. We called this cluster “Medium consumers-combination pattern.” Cluster 5 had the highest probability as compared to cluster 3 and 4 to eat all kinds of food, so we name it as “Higher consumers-combination pattern.” The estimated cluster sizes were 28.8% (n = 9990) for the “Healthy pattern,” 9.3% (n = 3216) for the “Western pattern,” 29.1% (n = 10100) for the “Lower consumers-combination pattern,” 28.7% (n = 9971) for the “Medium consumers-combination pattern” and 4.1% (n = 1430) for the “Higher consumers-combination pattern.” Descriptions of the clusters for selected variables are given in Table 5. The proportion of smokers to drinkers was higher in the Western pattern compared to the Healthy pattern. For the other three patterns, the proportion of smokers to drinkers gradually increased with increasing food consumption. A similar trend was shown in the other three subgroups (data not shown). Latent classes of severity of symptom Table 4 shows the conditional distribution of symptom group for all participants, giving the latent classes for the symptom groups more relevant in discriminating and labeling the clusters. The table of the normal control and the other three groups of the different stages of disease are given in the Supporting Information Table S4-S6. For all participants and for different stages of the disease, there were three classes with similar probability distributions. We named the first class “Asymptomatic” as subjects were reported to be relatively healthy and not showing any symptoms. Class 2 was named “Mild symptoms” as subjects in this cluster reported significant symptoms in terms of gingival bleeding. Subjects in the last class had a high percentage of symptoms reported in all areas. We named this class “Overt symptoms.” Sizes of the severity of symptom classes were 71.9% for the “Asymptomatic”, 26.7% for the “Mild symptoms” and 1.5% for the “Overt symptoms.” Further description of the severity of symptom classes for a selected set of variables are shown in Table 6. Differences between the clusters in demographics were not particularly significant. A similar trend was shown in the other three subgroups (data not shown). Logistic regression analysis and nomogram We applied an unconditional multivariable logistic regression to analyze dietary patterns as well as symptom severity for normal controls vs different stages of esophageal diseases. The results of the covariance diagnosis showed no significant covariance between the independent variables (data not shown). Table 7 and 8 shows the ORs and 95%CI for all stages of disease, by the classification in the five dietary patterns and three symptom severity classes from the composite model including the relevant confounding and risk variables. In the dietary patterns, compared to the “Healthy” pattern, the “Western”, the “Lower consumers-combination”, the “Medium consumers-combination” and the “Higher consumers-combination” were positively related to the risk of the progression of the disease stage for esophagitis (OR=1.42, 95%CI: 1.23-1.53; OR=2.33, 95%CI: 2.11-2.57; OR=1.99, 95%CI: 1.80-2.19 and OR=1.59, 95%CI: 1.32-1.92, respectively). Consistent results were also observed between the normal control, LGIN/HGIN and ESCC three groups. The “Western”, the “Lower consumers-combination”, the “Medium consumers-combination” and the “Higher consumers-combination” patterns showed a positive association with the risk of LGIN and HGIN and ESCC compared to the “Healthy” pattern. In the symptom severity classes, compared to the “Asymptomatic” class, the “Mild symptoms” class was positively related to the risk of the progression of the disease stage for the esophagitis and LGIN groups, OR=1.87, 95%CI: 1.66-2.10, OR=1.25, 95%CI: 1.11-1.41 for the “Mild symptoms” class and OR=1.04, 95%CI: 0.86-1.25 and OR=0.82, 95%CI: 0.51-1.31 for the “Overt symptoms” class. However, in the HGIN&ESCC group, the “Overt symptoms” class was positively related to the risk of HGIN&ESCC compared to the “Asymptomatic” class (OR=1.58, 95%CI: 1.00-2.50), The “Mild symptoms” class did not differ significantly from the “Asymptomatic” class (OR=0.98, 95%CI: 0.58-1.66). After logistic regression, we performed nomogram building and internal validation for each of the three subgroups. The model was virtually presented in the form of a nomogram (Figure 2), the C-index of the novel nomogram was 0.612, 0.684 and 0.746, respectively for esophagitis, LGIN and HGIN&ESCC groups, embodying the good predictive ability of the model. The calibration curves also showed good consistency in the probability between the actual observation and the nomogram prediction (Figure 3 a-c). In addition, decision curve analyses (DCA) exhibited great positive net benefits in the predictive model among almost all of the threshold probabilities at different groups, indicating the favorable potential clinical effect of the predictive model (Figure 3 d-f). Discussion This study is the first latent class analysis based on a natural population in a high incidence area of esophageal cancer in China. Additionally, this is the largest study to use latent class analysis to describe multiple dietary patterns and symptoms experienced by high incidence areas of esophageal cancer in China. Our objective was to identify dietary patterns and severity of symptoms conceived as mutually exclusive groups of people characterized by similar food intake and symptom clusters and to compare the resulting classes in terms of different stages of disease risk. We classified five dietary patterns. We discovered a protective effect on ESCC risk for a diet rich in vegetables and fruit consumption and associated with a low intake of red meat and pickles (Healthy pattern). Compared to this group, the “Western” pattern, distinguished by a low consumption of fruit and vegetables and high intake of red meat and pickles, was associated with an increased risk of esophagitis, LGIN, HGIN and ESCC. The“Lower consumers-combination” pattern, which showed a diet deficient in most of the foods considered, was positively related to the different stages of disease risk. The higher caloric intake of the “Higher consumers-combination” pattern, leads to an increased risk of the different stages of disease. And the “Medium consumers-combination” pattern is somewhere in between. Our results are consistent with most of the studies evaluating the influence of diet on ESCC risk [ 16 ]. The pattern with a high intake of fruit and vegetables, named “Healthy” or “Vegetable and Fruit” was found and associated with a decrease of ESCC risk in most of these studies [ 17 – 20 ]. A potential adverse effect of a diet mainly based on red meat, processed food in general was also found [ 21 , 22 ]. In a previous study, despite different labelling, number of components and weighting, the most commonly classified patterns were an ‘unhealthy’ one (often named ‘Western’) and a ‘healthy’ one in different country [ 23 ]. The other three patterns were not common in previous studies. The“Lower consumers-combination” pattern, is usually composed of people whose dietary intake is insufficient. Previous studies have reported a significantly increased risk of esophageal cancer in malnourished populations [ 24 ]. For the “Higher consumers-combination” pattern, the increased risk of esophageal cancer may be due to the high caloric intake of food. And a case-control study conducted in an Iranian population showed that higher intake of calories and total fat significantly increased the risk of esophageal cancer [ 25 ]. The increased risk of esophageal cancer in the “Medium consumers-combination” pattern may be due to the high intake of red meat and processed foods and relatively low intake of fruits and vegetables. The reasons for similar results in the subgroup analysis can be explained in the same way. We classified three symptom severity classes defined as mutually exclusive groups of subjects characterized by different symptoms. We found that at the stage of esophagitis and LGIN, the patient has only mild symptoms, while at the stage of HGIN and ESCC, the patient has relatively severe symptoms. If symptoms can be identified early in the disease process, the development of the disease can be reduced. In esophagitis and in the LGIN stage, compared to the “Asymptomatic” class, the “Mild symptoms” class may experience tooth loss and frequent gingival bleeding. In a study of risk factors for esophageal cancer and its precancerous lesions conducted in Henan, China, a high number of missing teeth was found to be a significant risk factor [ 26 ]. In another studies of a Chinese population, tooth loss was found to be a risk factor for esophageal squamous cell cancer [ 27 ] as well as gastric cancer [ 28 ]. The hypothesis, frequently cited in the ESCC etiology literature, is that incomplete chewing and rapid swallowing of large pieces of food might lead to irritation or damage to the esophageal epithelium and subsequently increase the risk of ESCC [ 29 ]. In the current study, the relationship between the number of teeth lost and ESCC risk supports this hypothesis. Another hypothesis is that poor oral hygiene and tooth loss mediate a bacterial load and “overgrowth” of microorganisms on teeth [ 30 ], which can transform nitrates into nitrites and then combine with amines to form carcinogenic nitrosamines, some of which may be gastrointestinal organ-specific carcinogens [ 31 , 32 ]. Therefore, an association between tooth loss and esophageal disease seems plausible. For people with frequent gingival bleeding, the increased risk of esophageal disease may be due to poor oral health as a result of bleeding gums. And many studies have shown that poor oral health is a risk factor for the development of esophageal cancer [ 33 – 36 ]. In the HGIN and ESCC group, compared to the “Asymptomatic” class, the “Overt symptoms” class has an increased risk of HGIN and ESCC. In this class, each symptom has a certain percentage of occurrence, especially upper gastrointestinal symptoms such as dysphagia, nausea, vomiting, etc. This indicates that upper gastrointestinal symptoms are already present when esophageal squamous cell carcinoma occurs or is about to occur. This reinforces the importance of intervention at an early stage of the disease. LCA can bring interesting insights into dietary patterning and symptom severity classes, allowing to identify prevalent types of eating behavior and severity of symptom in a population and to compare risk for people with different types of diet and symptom severity. Traditional methods such as PCA and FA, which have the disadvantage of not being able to produce mutually exclusive groups. Thus, when the interest is to compare subgroups of subjects, an additional step of cross-classification of the dimensions/combinations is needed. The application of a LCA to this study on different stages of esophageal disease overcomes problems inherent in the traditional methods and gives further advantages in dietary patterning and symptom severity class, such as a probability-based classification under a general parametric approach and pattern prevalence estimation. A nomogram is a convenient tool to anticipate and quantify the chance of an individual patient progressing to a certain clinical event. Nomograms are helpful in clinical decision making and valuable in risk stratification and individualized treatment [ 37 ]. Based on the nomogram, we can calculate the individual risk score and thus estimate the individual risk of disease. The identification of individuals as patients with early-stage disease, coupled with timely interventions, leads to a decrease in the incidence of ESCC. In this study, the nomogram we created had a good predictive power. Using, age, gender, education, BMI, smoking status, drinking status, dietary patterns, and symptom severity to build the model, it exhibited great positive net benefits. A potential limitation of our study is recall bias, which is the common issue in cross-sectional studies. The community-level recruitment approach in this report reduces but does not eliminate this source of bias. In addition, our model has only been internally validated, and the model will need to be further externally validated in the future if possible. The main strength of our study is that the study population is a natural multicenter population with a high incidence of esophageal squamous cell carcinoma in China. Moreover, we have explored both ESCC and different stages of early esophageal lesions. The detailed questionnaire was collected in a standardized data entry system by trained investigator to assure the quality of the data. Conclusions In conclusion, LCA gives further insights into dietary patterns and symptoms research, allowing for the definition and estimation of the prevalence of different groups of subjects characterized by different dietary choices and symptoms, and comparing those groups in relation to important health outcomes. At the same time, using the results of LCA analysis to establish a prediction model to screen high-risk individuals at the early stage of esophageal lesions, and to take intervention measures can effectively decrease the incidence and mortality of ESCC. Furthermore, cohort studies are needed to verify and expand our understanding. Abbreviations ESCC: esophageal squamous cell carcinoma LCA: latent class analysis ORs: odds ratio CIs: confidence intervals EADC: Esophageal adenocarcinoma EPL: esophageal precancerous lesions LGIN: low-grade intraepithelial neoplasia HGIN: high-grade intraepithelial neoplasia PCA: principal component analysis FA: factor analysis ESSH: esophageal squamous simple hyperplasia ESD: esophageal squamous dysplasia AIC: Akaike information criterion BIC: Bayesian information criterion BMI: body mass index Declarations Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Ethics statement The study was approved by the independent ethics committee of National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (approval no. 17-124/1380) and ethics committee of Capital Medical University (approval no. Z2019SY012). All participants signed informed consent. All the experiment protocol for involving humans was in accordance to guidelines of Declaration of Helsinki. Consent for publication Not applicable Competing interests The authors declare that they have no competing interests. Funding This work was supported by the National Key R&D Program of China (grant numbers 2016YFC1302804, 2016YFC1302800, 2016YFC0901400); National Natural Science Foundation of China (grant numbers 81874277, 81974493); CAMS Innovation Fund for Medical Sciences (grant numbers 2016-I2M-1-001, 2017-I2M-1-001, 2019-I2M-2-004); and Sanming Project of Medicine in Shenzhen (grant number SZSM201911008). Authors' contributions W GQ, LF, Y AM and G XH designed this study. LY, W JL, L YQ, Z SK, Z YZ, Z LW, Z DL, L FG, C LN, W XZ, Z CL, S GH, Z ZY, L YP, YZ, W YX and G YY contributed to the data collection and organized the database. Z ZP, NC and FW analyzed the data. Z ZP draft the manuscript. 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Tables Table 1 Model fit information for latent class analysis of dietary patterns Subtype Model AIC BIC Entropy LMR P-value ALMR P-value Overall 1-Class 403812.534 403930.899 2-Class 386659.699 386904.886 0.597 0.0000 0.0000 3-Class 380960.196 381332.203 0.628 0.0000 0.0000 4-Class 377988.054 378486.881 0.672 0.0000 0.0000 5-Class 376775.273 377400.920 0.648 0.0000 0.0000 6-Class 376013.990 376766.458 0.630 0.0499 0.0506 7-Class 375382.845 376262.134 0.616 1.0000 1.0000 Esophagitis 1-Class 383546.414 383664.057 2-Class 367382.696 367626.386 0.596 0.0000 0.0000 3-Class 361925.857 362295.594 0.629 0.0000 0.0000 4-Class 359187.987 359683.771 0.671 0.0000 0.0000 5-Class 358056.094 358677.925 0.648 0.0000 0.0000 6-Class 357318.205 358066.082 0.632 1.0000 1.0000 LGIN 1-Class 354511.583 354628.136 2-Class 339894.092 340135.524 0.590 0.0000 0.0000 3-Class 335082.275 335448.584 0.619 0.0000 0.0000 4-Class 332591.286 333082.474 0.662 0.0000 0.0000 5-Class 331522.734 332138.800 0.638 0.0000 0.0000 6-Class 330813.727 331554.671 0.623 0.0666 0.0674 7-Class 330280.873 331146.696 0.605 0.7604 0.7604 HGIN and ESCC 1-Class 341936.596 342052.644 2-Class 327933.965 328174.349 0.588 0.0000 0.0000 3-Class 323291.949 323656.671 0.620 0.0000 0.0000 4-Class 321000.163 321489.221 0.659 0.0000 0.0000 5-Class 319970.157 320583.552 0.638 0.0000 0.0000 6-Class 319270.147 320007.879 0.626 0.0687 0.0696 7-Class 318754.107 319616.176 0.605 0.5446 0.5464 Note: LGIN: low-grade intraepithelial neoplasia; HGIN: high-grade intraepithelial neoplasia; ESCC: esophageal squamous cell carcinoma; AIC: akaike information criterion; BIC: bayesian information criterion; LMR: Lo–Mendell–Rubin likelihood ratio test; ALMR: Lo–Mendell–Rubin Adjusted LMR test Table 2 Model fit information for latent class analysis of symptom classes Subtype Model AIC BIC Entropy LMR P-value ALMR P-value Overall 1-Class 136196.132 136255.315 2-Class 134911.554 135038.375 0.898 0.0000 0.0000 3-Class 134613.394 134807.852 0.935 0.0000 0.0000 4-Class 134595.780 134857.876 0.623 0.1470 0.1508 Esophagitis 1-Class 129391.888 129450.710 2-Class 128168.972 128295.018 0.896 0.0000 0.0000 3-Class 127893.905 128087.176 0.821 0.0000 0.0000 4-Class 127881.486 128141.983 0.620 0.5484 0.5522 LGIN 1-Class 120040.246 120098.523 2-Class 118822.702 118947.580 0.890 0.0000 0.0000 3-Class 118587.299 118778.779 0.932 0.0000 0.0000 4-Class 118569.312 118827.393 0.648 0.0750 0.0778 HGIN and ESCC 1-Class 115838.134 115896.158 2-Class 114651.925 114776.262 0.889 0.0000 0.0000 3-Class 114426.629 114617.279 0.850 0.0000 0.0000 4-Class 114414.785 114671.748 0.688 0.1674 0.1721 Note: LGIN: low-grade intraepithelial neoplasia; HGIN: high-grade intraepithelial neoplasia; ESCC: esophageal squamous cell carcinoma; AIC: akaike information criterion; BIC: bayesian information criterion; LMR: Lo–Mendell–Rubin likelihood ratio test; ALMR: Lo–Mendell–Rubin Adjusted LMR test Table 3 Probabilities of consumption for selected food items by dietary patterns derived from LCA for all participants Healthy Western Lower consumers- combination Medium consumers- combination Higher consumers- combination Livestock meat everyday 77.1% 84.7% 14.8% 9.8% 54.0% 1-6days/week 12.5% 12.6% 22.3% 72.2% 43.3% <1day/week 10.4% 2.7% 62.9% 18.0% 2.7% Poultry meat ≥1day/week 7.0% 7.4% 1.5% 3.3% 89.1% <1day/week 93.0% 92.6% 98.5% 96.7% 10.9% Seafood ≥1day/week 1.5% 9.1% 1.5% 0.3% 64.8% <1day/week 98.5% 90.9% 98.5% 99.7% 35.2% Eggs and their products everyday 61.3% 73.1% 22.0% 31.9% 50.1% 1-6days/week 15.2% 16.4% 24.1% 64.5% 38.4% <1day/week 23.5% 10.5% 53.9% 3.6% 11.5% Vegetables everyday 99.8% 0.0% 2.5% 2.7% 97.2% 1-6days/week 0.1% 0.6% 2.6% 97.3% 2.3% <1day/week 0.1% 99.4%% 94.9% 0.0% 0.5% Fruits everyday 41.4% 18.6% 3.0% 2.8% 31.8% 1-6days/week 21.0% 41.4% 11.9% 59.5% 54.8% <1day/week 37.6% 40.0% 85.1% 37.7% 13.4% Bean products ≥1day/week 38.0% 46.3% 8.6% 45.3% 65.7% <1day/week 62.0% 53.7% 91.4% 54.7% 34.3% Scallion, ginger and garlic ≥1day/week 93.7% 72.7% 36.6% 59.9% 84.7% <1day/week 6.3% 27.3% 63.4% 40.1% 15.3% Pickles ≥1day/week 4.8% 68.8% 3.1% 11.7% 17.9% <1day/week 95.2% 31.2% 96.9% 88.3% 82.1% Nut fruits ≥1day/week 16.9% 33.8% 2.1% 5.0% 24.2% <1day/week 83.1% 66.2% 97.9% 95.0% 75.8% Cluster’s size 28.8% 9.3% 29.1% 28.7% 4.1% Table 4 Probabilities of consumption for selected symptom items by severity of symptom derived from LCA for all participants Asymptomatic Mild symptoms Overt symptoms Number of lost teeth 0 teeth 48.4% 39.5% 32.8% 1-3 teeth 29.5% 39.3% 31.6% more than 4 teeth 22.1% 21.1% 35.6% Whether gingival bleeding No 100.0% 0.0% 64.9% Yes 0.0% 100.0% 35.1% Whether dysphagia Yes 0.2% 0.1% 7.0% No 99.8% 99.9% 93.0% Whether bloating, heartburn, acid reflux Yes 1.6% 1.4% 47.6% No 98.4% 98.6% 52.4% Whether nausea, vomiting and belching Yes 0.6% 1.0% 36.6% No 99.4% 99.0% 63.4% Whether epigastric pain Yes 0.8% 0.8% 20.1% No 99.2% 99.2% 79.9% Cluster’s size 71.9% 26.7% 1.5% Table 5 Dietary patterns’ characteristics according to selected sociodemographic variables for all participants Healthy Western Lower consumers- combination Medium consumers- combination Higher consumers- combination Normal control 89.1% 84.0% 79.7% 82.4% 84.1% Esophagitis 6.9% 9.7% 14.7% 12.6% 10.4% LGIN 3.3% 5.1% 4.5% 4.0% 4.5% HGIN and ESCC 0.7% 1.2% 1.1% 1.0% 1.0% Age 60 22.3% 26.7% 30.0% 25.3% 24.9% Gender Male 41.3% 46.4% 40.0% 38.9% 46.0% Female 58.7% 53.6% 60.0% 61.1% 54.0% Education Never been to school 14.9% 13.3% 16.1% 14.5% 12.0% Primary school 34.3% 33.0% 39.6% 35.4% 30.8% Middle school 39.8% 43.9% 35.6% 38.7% 42.2% High school and above 11.0% 9.8% 8.7% 11.4% 15.0% BMI 27.0 11.9% 13.8% 9.9% 12.5% 10.1% Smoking status Never smoked 74.9% 64.5% 77.5% 77.5% 73.2% Current smoking 20.6% 27.1% 19.9% 18.4% 22.0% Previous smoking 4.5% 8.4% 2.7% 4.1% 4.8% Drinking status No alcohol consumption 62.0% 57.6% 75.6% 63.9% 64.3% <1 time/week 28.8% 20.1% 19.5% 29.1% 20.0% ≥1 time/week 9.2% 22.3% 4.95% 7.0% 15.7% Note: LGIN: low-grade intraepithelial neoplasia; HGIN: high-grade intraepithelial neoplasia; ESCC: esophageal squamous cell carcinoma; BMI: body mass index. Table 6 Severity of symptoms’ characteristics according to selected sociodemographic variables for all participants Asymptomatic Mild symptoms Overt symptoms Normal control 84.0% 82.8% 89.1% Esophagitis 11.0% 11.9% 5.9% LGIN 4.0% 4.5% 3.8% HGIN and ESCC 1.0% 0.8% 1.2% Age 60 26.6% 23.5% 33.6% Gender Male 41.9% 38.5% 36.6% Female 58.1% 61.5% 63.4% Education Never been to school 15.0% 13.8% 25.5% Primary school 35.0% 38.4% 33.6% Middle school 39.0% 38.4% 29.4% High school and above 11.0% 9.4% 11.5% BMI 27.0 12.1% 10.5% 8.7% Smoking status Never smoked 74.2% 78.5% 74.9% Current smoking 21.5% 17.6% 19.0% Previous smoking 4.3% 3.9% 6.1% Drinking status No alcohol consumption 65.5% 67.9% 70.6% <1 time/week 24.6% 26.0% 25.7% ≥1 time/week 9.9% 6.1% 3.8% Note: LGIN: low-grade intraepithelial neoplasia; HGIN: high-grade intraepithelial neoplasia; ESCC: esophageal squamous cell carcinoma; BMI: body mass index. Table 7 Odds ratios (OR) and the corresponding 95% confidence intervals (CIs) of esophageal cancer for each cluster of dietary patterns adjusting for known confounders: per disease stage Adjusted OR(95%CI) Disease stage Esophagitis LGIN HGIN and ESCC Case/Control 3883/29079 1413/29079 332/29079 Dietary Patterns Healthy 1 1 1 Western 1.42(1.23-1.53) 1.54(1.27-1.87) 1.69(1.14-2.52) Lower consumers-combination 2.33(2.11-2.57) 1.39(1.20-1.61) 1.72(1.26-2.36) Medium consumers-combination 1.99(1.80-2.19) 1.30(1.12-1.51) 1.64(1.19-2.24) Higher consumers-combination 1.59(1.32-1.92) 1.44(1.09-1.90) 1.62(0.92-2.86) Note: LGIN: low-grade intraepithelial neoplasia; HGIN: high-grade intraepithelial neoplasia; ESCC: esophageal squamous cell carcinoma. Table 8 Odds ratios (OR) and the corresponding 95% confidence intervals (CIs) of esophageal cancer for each cluster of symptom clusters adjusting for known confounders: per disease stage Adjusted OR(95%CI) Disease stage Esophagitis LGIN HGIN and ESCC Case/Control 3883/29079 1413/29079 332/29079 Severity of symptom Asymptomatic 1 1 1 Mild symptoms 1.87(1.66-2.10) 1.25(1.11-1.41) 0.98(0.58-1.66) Overt symptoms 1.04(0.86-1.25) 0.82(0.51-1.31) 1.58(1.00-2.50) Note: LGIN: low-grade intraepithelial neoplasia; HGIN: high-grade intraepithelial neoplasia; ESCC: esophageal squamous cell carcinoma. Additional Declarations No competing interests reported. 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09:29:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1038856/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1038856/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":15402338,"identity":"0f7dd8ed-4fe7-4c8b-afa6-297360bbb8ba","added_by":"auto","created_at":"2021-11-10 16:21:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":14846,"visible":true,"origin":"","legend":"Flow chart of participant selection.\nNote: LGIN = low-grade intraepithelial neoplasia; HGIN = high-grade intraepithelial neoplasia; ESCC = esophageal squamous cell carcinoma.\n","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-1038856/v1/f8b41e8799cc9c033d06a73b.png"},{"id":15402080,"identity":"3a0ddf01-fdde-4068-91c9-d6d17030fb47","added_by":"auto","created_at":"2021-11-10 16:18:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":226212,"visible":true,"origin":"","legend":"A: Nomogram predicting the risk for the normal control group vs esophagitis; B: Nomogram predicting the risk for the normal control group vs LGIN; C: Nomogram predicting the risk for the normal control group vs HGIN and ESCC.\nNote: LGIN: low-grade intraepithelial neoplasia; HGIN: high-grade intraepithelial neoplasia; ESCC: esophageal squamous cell carcinoma.\n","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-1038856/v1/25a748516f96414bd11fab1c.png"},{"id":15402339,"identity":"b1e32998-463a-4ffa-b02d-5ea88ba9b5eb","added_by":"auto","created_at":"2021-11-10 16:21:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":414392,"visible":true,"origin":"","legend":"A-C: Calibration curves showing the probability of the normal control vs esophagitis/LGIN/HGIN and ESCC three groups between the nomogram prediction and the actual observation. Perfect prediction would correspond to a slope of 1 (diagonal 45-degree gray line). D-F: Decision curves of the nomogram predicting the risk. The x-axis represents the threshold probabilities, and the y-axis measures the net benefit calculated by adding the true positives and subtracting the false positives.\nNote: LGIN: low-grade intraepithelial neoplasia; HGIN: high-grade intraepithelial neoplasia; ESCC: esophageal squamous cell carcinoma.\n","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-1038856/v1/8f4102ea27d8ea6a153d5f3d.png"},{"id":15402340,"identity":"93d762f0-a7bc-4745-8a81-4ffb726f37ac","added_by":"auto","created_at":"2021-11-10 16:21:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1455925,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1038856/v1/36902808-b757-430e-8556-59244d20148e.pdf"},{"id":15402078,"identity":"655b01d6-d731-4a33-acef-ad1b83305e35","added_by":"auto","created_at":"2021-11-10 16:18:51","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":24978,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-1038856/v1/f6b189510882a67b09d5c2de.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eDietary Patterns and Severity of Symptom with the Risk of Esophageal Squamous Cell Carcinoma and its Histological Precursor Lesions in China: A Multicenter Cross-Sectional Latent Class Analysis\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eEsophageal cancer ranks seventh in terms of incidence and sixth in mortality overall in the world according to the report of the Global Cancer Statistics 2020. Eastern Asia shows the highest regional incidence rates for both men and women, partly because of the large burden in China [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In China, esophageal cancer is the sixth most common malignancy and the fourth leading cause of cancer-related deaths [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Esophageal adenocarcinoma (EADC) and esophageal squamous cell carcinoma (ESCC) are the two most common histologic subtypes of esophageal cancer. In China, more than 90% of esophageal cancer cases are ESCCs, whose number accounts for about half of all the ESCC cases on earth [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The most surveyed region of China is the North Central Taihung Mountain range. In small areas of this region, ESCC may be at or nearly the leading cause of death, with incidence rates exceeding 125/100,000 per year [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Therefore, identification of risk factors in the early stages of the disease appears to be essential in order to decrease ESCC incidence and mortality.\u003c/p\u003e \u003cp\u003eEsophagitis is a precancerous disease of esophageal cancer, and it is a benign disease with a certain canceration rate. Based on WHO tumor histological classification, the esophageal precancerous lesions (EPL) can be defined as low-grade intraepithelial neoplasia (LGIN) and high-grade intraepithelial neoplasia (HGIN) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. With the aggravation of precancerous lesions, the rate of developing esophageal cancer increased from 24\u0026ndash;74% [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Therefore, intervention at an early stage of the disease results in a significant decrease in ESCC incidence and mortality. The etiology of ESCC is multi-factorial and strongly population dependent. A study estimated a population-attributable risk of 89% using only cigarette smoking, alcoholic beverage consumption, and low consumption of fruits and vegetables [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. There is also some evidence on the protective effect of fruit and vegetable and the potential harmful effect of processed/red meat consumption. The evidence on the effect of diet on ESCC risk is however still suggestive or limited [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Before a diagnosis of esophageal cancer is established, majority of the patients have experienced pain, dysphagia, eating difficulties, appetite loss, bloating and nausea resulting in patients\u0026rsquo; daily living and quality of life [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Therefore, it is more meaningful for the prevention of esophageal cancer that identifying the high risk dietary patterns and some classical symptoms in the early stage of esophageal cancer.\u003c/p\u003e \u003cp\u003eThe analysis of dietary patterns aims to fully explore the complexity of the diet, as an alternative to the study of isolated components. These techniques depend upon the concept that food consumption can be effectively presented by reproducible patterns, in spite of individual variations, and that food eaten together may have interactive effects on the risk of cancer. Similarly, the symptoms are the same. Identifying symptom clusters and their relationship to patient characteristics may lead to a better interpretation for identifying patients with early lesions and provide greater insight into the planning of future interventions.\u003c/p\u003e \u003cp\u003eLatent class analysis (LCA) is a model-based cluster analysis technique that allows for identifying prevalent, mutually exclusive, eating behaviors and symptoms with additional advantages with respect to the classical approaches [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Unlike principal component analysis (PCA) and factor analysis (FA), it can be used to categorize individuals into mutually exclusive groups, dietary patterns or severity of symptoms and differently from cluster analysis, it grants quantification of the uncertainty of class membership, and assessment of goodness of fit [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study aims to identify dietary patterns and severity of symptom through LCA, and thus to model screening for different stages of the disease. Therefore, adding a new perspective on the association between dietary habits and symptoms and ESCC in China.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eStudy population\u003c/h2\u003e\n\u003cp\u003eThis was a multicenter cross-sectional study, depending on high incidence regions of esophageal and gastric cancer established by the cancer early diagnosis and early treatment project in China [12]. In 2017, a new screening study of upper gastrointestinal cancer in five high-risk regions of upper gastrointestinal cancer in China, including Hebei, Henan, Shandong, Shanxi, and Gansu Provinces was released. The main purpose of this project was to confirm the high-risk population of malignant upper gastrointestinal cancer and to establish a cancer risk prediction model to provide support for the prevention of upper gastrointestinal cancer.\u003c/p\u003e\n\u003cp\u003eThe inclusion criteria were as follows: (1) local permanent residents in selected regions, (2) no history of endoscopic examination during the last 3 years, (3) no history of cancer, mental disorder, or any contraindication for endoscopy, (4) signed informed consent and (5) agreement to complete the entire survey and examination, including endoscopy. The participant selection process is shown in Figure 1. We recruited participants from April 2017 to December 2018. The final analysis included 34,707 residents aged 40-69 years. Among these participants, there were 81 persons with ESCC, 251 persons with HGIN, 1,413 persons with LGIN, 3,883 persons with esophagitis and 29,079 persons serving as normal esophagus controls.\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Capital Medical University, Chinese Academy of Medical Sciences and Peking Union Medical College. The experimental protocol involving humans was in accordance to the guidelines of the Declaration of Helsinki.\u003c/p\u003e\n\u003ch2\u003eDiet and symptoms assessment\u003c/h2\u003e\n\u003cp\u003eComprehensive questionnaire information was collected by face-to-face interviews and entered directly into a laptop based data entry system by trained investigators. The data entry process was conducted with software designed to decrease missing items and reduce logic inaccuracy. A questionnaire typically took 35-45 minutes to complete. Items of dietary intake were selected through the above questionnaire, including livestock meat, poultry\u0026nbsp;meat, seafood, eggs and their products, vegetables, fruits, bean\u0026nbsp;products, scallions,\u0026nbsp;ginger\u0026nbsp;and\u0026nbsp;garlic,\u0026nbsp;pickles and nuts. All the variables were categorical. Foods that are consumed more frequently, were divided into three categories namely: every day, 1-6 days per week, and less than 1 day per week. Foods that are consumed infrequently, were divided into two categories namely: at least one day per week and less than one day per week. Items of typical symptoms were also selected through the above questionnaire, including number of lost teeth, frequent bleeding of gums, dysphagia, bloating, heartburn, acid reflux, nausea, vomiting, belching and epigastric pain. The number of lost teeth were categorized into three groups namely: never dropped, 1-3 teeth and more than 4 teeth. Other variables are categorized as yes or no.\u003c/p\u003e\n\u003ch2\u003eOutcome assessment\u003c/h2\u003e\n\u003cp\u003eThe endoscopic examinations were carried out by physicians at local hospitals. Procedures were based on clinical guidelines for cancer screening and early diagnosis and treatment in China. Lugol\u0026rsquo;s iodine staining method was used to identify suspicious tissues, which were then biopsied. To confirm severity, the esophageal mucosa was ranked into 5 categories: normal esophageal mucosa, minor mucosa changes, esophagitis, esophageal squamous simple hyperplasia (ESSH) or esophageal squamous dysplasia (ESD)\u0026nbsp;[13]. ESD was further classified into 3 levels including slight, moderate, and severe. According to WHO tumor histological classification, mild and moderate ESD combined fall under LGIN. Severe ESD and squamous cell carcinoma in situ are considered as HGIN. If there were any inconsistencies, a third pathologist would give advice through discussion. Doctors reported the worst biopsy diagnosis to be from participants with multiple lesions. In this study, we divided the participants into 4 groups: normal control, esophagitis, LGIN and HGIN/ESCC.\u003c/p\u003e\n\u003ch2\u003eStatistical methods\u003c/h2\u003e\n\u003cp\u003eWe characterized the dietary patterns and symptom patterns, assumed as unobserved mutually exclusive, with different variables probability distributions, by performing LCA on the observed responses on the different items.\u003c/p\u003e\n\u003cp\u003eLCA identified latent classes of participants based on the ten dietary variables and six symptom variables. Estimation was conducted with the robust maximum-likelihood and expectation-maximization algorithms\u0026nbsp;[14]. Statistical fit indexes were used to assess model fit and to decide the final number of latent classes. The model that fits the data best was selected by a combination of the following criteria: (a) the lowest Akaike information criterion (AIC), (b) the lowest Bayesian information criterion (BIC), (c) the lowest Lo\u0026ndash;Mendell\u0026ndash;Rubin likelihood ratio test (LMR), (d) the lowest Lo\u0026ndash;Mendell\u0026ndash;Rubin Adjusted LMR test (ALMR), and (e) entropy to be 0.6 or greater\u0026nbsp;[15]. Next, we executed an\u0026nbsp;unconditional multivariable logistic regression\u0026nbsp;to identify sociodemographic and risk factors that predicted class membership. Before conducting the analysis, we performed a covariance diagnosis between the independent variables. We considered models to calculate the adjusted\u0026nbsp;odds ratios (ORs) and 95% confidence intervals (CIs), including age, gender, education, body mass index (BMI), smoking and drinking at the same time. We also used a nomogram to model normal controls separately from the different stages of the disease. Evaluation of the model was performed using calibration curves and decision curves. In addition, basic, descriptive statistics show categorical variables as percentages, while continuous variables are shown as mean and standard deviations.\u003c/p\u003e\n\u003cp\u003eLCA was conducted in both cases and normal controls. Analysis of only the normal control was performed to check the robustness of the previous solution. As dietary patterns and symptom severity classes identified on controls were consistent (number and characteristics of the patterns) with the ones obtained on the overall dataset, we based all our analysis on the overall dataset. To guarantee the internal reproducibility of the chosen solution the analysis was conducted separately in two randomly selected subsets of the original data several times.\u003c/p\u003e\n\u003cp\u003eStatistical analyses were performed using Mplus (version 8.1) and R (version 3.6.3) software. All tests were two-sided and had a significance level of 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eWhen fitting the LCA model, we selected the result with 5 classes for dietary patterns and 3 classes for the severity of symptom in every group according to the criteria in methods (Table 1 and 2).\u003c/p\u003e\n\u003ch2\u003eLatent classes of dietary patterns\u003c/h2\u003e\n\u003cp\u003eTable 3 shows the conditional distribution of food group intake for all\u0026nbsp;participants, giving the latent classes for the food groups that were more relevant in discriminating and labeling the clusters. The table of the normal control and the other three groups of different stages of disease are given in the Supporting Information Table S1-S3. We identified five classes with similar probability distributions according to a previous study\u0026nbsp;[10], both for all participants and for different stages of the disease. Cluster 1 labeled \u0026ldquo;Healthy pattern\u0026rdquo;, showed higher probability to consume more fruiting vegetables and all other kinds of fruits, high-quality protein and lower probability to consume red meat. Subjects in Cluster 2, \u0026ldquo;Western pattern,\u0026rdquo; reported higher consumption of red meat and lower consumption of vegetables and fruits. Clusters 3 to 5 were related to previous food groups, but with a difference in the amount of intake. We termed Cluster 3 \u0026ldquo;Lower consumers-combination pattern\u0026rdquo; as people in it were less likely to eat all kinds of food. Cluster 4 had slightly higher probability than cluster 3 to eat all kinds of food. We called this cluster \u0026ldquo;Medium consumers-combination pattern.\u0026rdquo; Cluster 5 had the highest probability as compared to cluster 3 and 4 to eat all kinds of food, so we name it as \u0026ldquo;Higher consumers-combination pattern.\u0026rdquo; The estimated cluster sizes were 28.8% (n = 9990) for the \u0026ldquo;Healthy pattern,\u0026rdquo; 9.3% (n = 3216) for the\u0026nbsp;\u0026ldquo;Western pattern,\u0026rdquo; 29.1% (n = 10100) for the \u0026ldquo;Lower consumers-combination pattern,\u0026rdquo; 28.7% (n = 9971) for the \u0026ldquo;Medium consumers-combination pattern\u0026rdquo; and 4.1% (n = 1430) for the \u0026ldquo;Higher consumers-combination pattern.\u0026rdquo;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDescriptions of the clusters for selected variables are given in Table 5. The proportion of smokers to drinkers was higher in the Western pattern compared to the Healthy pattern. For the other three patterns, the proportion of smokers to drinkers gradually increased with increasing food consumption. A similar trend was shown in the other three subgroups (data not shown).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eLatent classes of severity of symptom\u003c/h2\u003e\n\u003cp\u003eTable 4 shows the conditional distribution of symptom group for all\u0026nbsp;participants, giving the latent classes for the symptom groups more relevant in discriminating and labeling the clusters. The table of the normal control and the other three groups of the different stages of disease are given in the Supporting Information Table S4-S6. For all participants and for different stages of the disease, there were three classes with similar probability distributions. We named the first class \u0026ldquo;Asymptomatic\u0026rdquo; as subjects were reported to be relatively healthy and not showing any symptoms. Class 2 was named \u0026ldquo;Mild symptoms\u0026rdquo; as\u0026nbsp;subjects in this cluster reported significant symptoms in terms of\u0026nbsp;gingival bleeding. Subjects in the last class had a high percentage of symptoms reported in all areas. We named this class \u0026ldquo;Overt symptoms.\u0026rdquo; Sizes of the severity of symptom classes were 71.9% for the\u0026nbsp;\u0026ldquo;Asymptomatic\u0026rdquo;, 26.7% for the\u0026nbsp;\u0026ldquo;Mild symptoms\u0026rdquo; and 1.5% for the \u0026ldquo;Overt\u0026nbsp;symptoms.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003eFurther description of the severity of symptom classes for a selected set of variables are shown in Table 6. Differences between the clusters in demographics were not particularly significant.\u0026nbsp;A similar trend was shown in the other three subgroups (data not shown).\u003c/p\u003e\n\u003ch2\u003eLogistic regression analysis and nomogram\u003c/h2\u003e\n\u003cp\u003eWe applied\u0026nbsp;an\u0026nbsp;unconditional multivariable logistic regression\u0026nbsp;to analyze dietary patterns as well as symptom severity for normal controls vs different stages of esophageal diseases. The results of the covariance diagnosis showed no significant covariance between the independent variables (data not shown). Table 7 and 8 shows the ORs and 95%CI for all stages of disease, by the classification in the five dietary patterns and three symptom severity classes from the composite model including the relevant confounding and risk variables. In the dietary patterns, compared to the \u0026ldquo;Healthy\u0026rdquo; pattern, the \u0026ldquo;Western\u0026rdquo;, the \u0026ldquo;Lower consumers-combination\u0026rdquo;, the \u0026ldquo;Medium consumers-combination\u0026rdquo; and the \u0026ldquo;Higher consumers-combination\u0026rdquo;\u0026nbsp;were positively related to the risk of the progression of the disease stage for esophagitis\u0026nbsp;(OR=1.42, 95%CI: 1.23-1.53; OR=2.33, 95%CI: 2.11-2.57; OR=1.99, 95%CI: 1.80-2.19 and OR=1.59, 95%CI: 1.32-1.92, respectively). Consistent results were also observed between the normal control, LGIN/HGIN and ESCC three groups. The \u0026ldquo;Western\u0026rdquo;, the \u0026ldquo;Lower consumers-combination\u0026rdquo;, the \u0026ldquo;Medium consumers-combination\u0026rdquo; and the \u0026ldquo;Higher consumers-combination\u0026rdquo; patterns showed a positive association with the risk of LGIN and HGIN and ESCC compared to the \u0026ldquo;Healthy\u0026rdquo; pattern. In the symptom\u0026nbsp;severity classes, compared to the \u0026ldquo;Asymptomatic\u0026rdquo; class, the \u0026ldquo;Mild symptoms\u0026rdquo; class was\u0026nbsp;positively related to the risk of the progression of the disease stage for\u0026nbsp;the esophagitis and LGIN groups, OR=1.87, 95%CI: 1.66-2.10, \u0026nbsp;OR=1.25, 95%CI: 1.11-1.41 for the \u0026ldquo;Mild symptoms\u0026rdquo; class and OR=1.04, 95%CI: 0.86-1.25 and OR=0.82, 95%CI: 0.51-1.31 for the\u0026nbsp;\u0026ldquo;Overt\u0026nbsp;symptoms\u0026rdquo; class. However, in the HGIN\u0026amp;ESCC group, the \u0026ldquo;Overt\u0026nbsp;symptoms\u0026rdquo; class was\u0026nbsp;positively related to the risk of HGIN\u0026amp;ESCC compared to the \u0026ldquo;Asymptomatic\u0026rdquo; class (OR=1.58, 95%CI: 1.00-2.50),\u0026nbsp;The \u0026ldquo;Mild\u0026nbsp;symptoms\u0026rdquo; class did not differ significantly from the\u0026nbsp;\u0026ldquo;Asymptomatic\u0026rdquo; class (OR=0.98, 95%CI: 0.58-1.66).\u003c/p\u003e\n\u003cp\u003eAfter logistic regression, we performed nomogram building and internal validation for each of the three subgroups. The model was virtually presented in the form of a nomogram (Figure 2), the C-index of the novel nomogram was 0.612, 0.684 and 0.746, respectively for\u0026nbsp;esophagitis, LGIN and HGIN\u0026amp;ESCC groups,\u0026nbsp;embodying the good predictive ability of the model. The calibration curves also showed good consistency in the probability between the actual observation and the nomogram prediction (Figure 3 a-c). In addition, decision curve analyses (DCA) exhibited great positive net benefits in the predictive model among almost all of the threshold probabilities at different groups, indicating the favorable potential clinical effect of the predictive model (Figure 3 d-f).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study is the first latent class analysis based on a natural population in a high incidence area of esophageal cancer in China. Additionally, this is the largest study to use latent class analysis to describe multiple dietary patterns and symptoms experienced by high incidence areas of esophageal cancer in China. Our objective was to identify dietary patterns and severity of symptoms conceived as mutually exclusive groups of people characterized by similar food intake and symptom clusters and to compare the resulting classes in terms of different stages of disease risk.\u003c/p\u003e \u003cp\u003eWe classified five dietary patterns. We discovered a protective effect on ESCC risk for a diet rich in vegetables and fruit consumption and associated with a low intake of red meat and pickles (Healthy pattern). Compared to this group, the \u0026ldquo;Western\u0026rdquo; pattern, distinguished by a low consumption of fruit and vegetables and high intake of red meat and pickles, was associated with an increased risk of esophagitis, LGIN, HGIN and ESCC. The\u0026ldquo;Lower consumers-combination\u0026rdquo; pattern, which showed a diet deficient in most of the foods considered, was positively related to the different stages of disease risk. The higher caloric intake of the \u0026ldquo;Higher consumers-combination\u0026rdquo; pattern, leads to an increased risk of the different stages of disease. And the \u0026ldquo;Medium consumers-combination\u0026rdquo; pattern is somewhere in between.\u003c/p\u003e \u003cp\u003eOur results are consistent with most of the studies evaluating the influence of diet on ESCC risk [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The pattern with a high intake of fruit and vegetables, named \u0026ldquo;Healthy\u0026rdquo; or \u0026ldquo;Vegetable and Fruit\u0026rdquo; was found and associated with a decrease of ESCC risk in most of these studies [\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. A potential adverse effect of a diet mainly based on red meat, processed food in general was also found [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In a previous study, despite different labelling, number of components and weighting, the most commonly classified patterns were an \u0026lsquo;unhealthy\u0026rsquo; one (often named \u0026lsquo;Western\u0026rsquo;) and a \u0026lsquo;healthy\u0026rsquo; one in different country [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The other three patterns were not common in previous studies. The\u0026ldquo;Lower consumers-combination\u0026rdquo; pattern, is usually composed of people whose dietary intake is insufficient. Previous studies have reported a significantly increased risk of esophageal cancer in malnourished populations [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. For the \u0026ldquo;Higher consumers-combination\u0026rdquo; pattern, the increased risk of esophageal cancer may be due to the high caloric intake of food. And a case-control study conducted in an Iranian population showed that higher intake of calories and total fat significantly increased the risk of esophageal cancer [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The increased risk of esophageal cancer in the \u0026ldquo;Medium consumers-combination\u0026rdquo; pattern may be due to the high intake of red meat and processed foods and relatively low intake of fruits and vegetables. The reasons for similar results in the subgroup analysis can be explained in the same way.\u003c/p\u003e \u003cp\u003eWe classified three symptom severity classes defined as mutually exclusive groups of subjects characterized by different symptoms. We found that at the stage of esophagitis and LGIN, the patient has only mild symptoms, while at the stage of HGIN and ESCC, the patient has relatively severe symptoms. If symptoms can be identified early in the disease process, the development of the disease can be reduced. In esophagitis and in the LGIN stage, compared to the \u0026ldquo;Asymptomatic\u0026rdquo; class, the \u0026ldquo;Mild symptoms\u0026rdquo; class may experience tooth loss and frequent gingival bleeding. In a study of risk factors for esophageal cancer and its precancerous lesions conducted in Henan, China, a high number of missing teeth was found to be a significant risk factor [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In another studies of a Chinese population, tooth loss was found to be a risk factor for esophageal squamous cell cancer [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] as well as gastric cancer [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The hypothesis, frequently cited in the ESCC etiology literature, is that incomplete chewing and rapid swallowing of large pieces of food might lead to irritation or damage to the esophageal epithelium and subsequently increase the risk of ESCC [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In the current study, the relationship between the number of teeth lost and ESCC risk supports this hypothesis. Another hypothesis is that poor oral hygiene and tooth loss mediate a bacterial load and \u0026ldquo;overgrowth\u0026rdquo; of microorganisms on teeth [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], which can transform nitrates into nitrites and then combine with amines to form carcinogenic nitrosamines, some of which may be gastrointestinal organ-specific carcinogens [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Therefore, an association between tooth loss and esophageal disease seems plausible. For people with frequent gingival bleeding, the increased risk of esophageal disease may be due to poor oral health as a result of bleeding gums. And many studies have shown that poor oral health is a risk factor for the development of esophageal cancer [\u003cspan additionalcitationids=\"CR34 CR35\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In the HGIN and ESCC group, compared to the \u0026ldquo;Asymptomatic\u0026rdquo; class, the \u0026ldquo;Overt symptoms\u0026rdquo; class has an increased risk of HGIN and ESCC. In this class, each symptom has a certain percentage of occurrence, especially upper gastrointestinal symptoms such as dysphagia, nausea, vomiting, etc. This indicates that upper gastrointestinal symptoms are already present when esophageal squamous cell carcinoma occurs or is about to occur. This reinforces the importance of intervention at an early stage of the disease.\u003c/p\u003e \u003cp\u003eLCA can bring interesting insights into dietary patterning and symptom severity classes, allowing to identify prevalent types of eating behavior and severity of symptom in a population and to compare risk for people with different types of diet and symptom severity. Traditional methods such as PCA and FA, which have the disadvantage of not being able to produce mutually exclusive groups. Thus, when the interest is to compare subgroups of subjects, an additional step of cross-classification of the dimensions/combinations is needed. The application of a LCA to this study on different stages of esophageal disease overcomes problems inherent in the traditional methods and gives further advantages in dietary patterning and symptom severity class, such as a probability-based classification under a general parametric approach and pattern prevalence estimation.\u003c/p\u003e \u003cp\u003eA nomogram is a convenient tool to anticipate and quantify the chance of an individual patient progressing to a certain clinical event. Nomograms are helpful in clinical decision making and valuable in risk stratification and individualized treatment [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Based on the nomogram, we can calculate the individual risk score and thus estimate the individual risk of disease. The identification of individuals as patients with early-stage disease, coupled with timely interventions, leads to a decrease in the incidence of ESCC. In this study, the nomogram we created had a good predictive power. Using, age, gender, education, BMI, smoking status, drinking status, dietary patterns, and symptom severity to build the model, it exhibited great positive net benefits.\u003c/p\u003e \u003cp\u003eA potential limitation of our study is recall bias, which is the common issue in cross-sectional studies. The community-level recruitment approach in this report reduces but does not eliminate this source of bias. In addition, our model has only been internally validated, and the model will need to be further externally validated in the future if possible. The main strength of our study is that the study population is a natural multicenter population with a high incidence of esophageal squamous cell carcinoma in China. Moreover, we have explored both ESCC and different stages of early esophageal lesions. The detailed questionnaire was collected in a standardized data entry system by trained investigator to assure the quality of the data.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, LCA gives further insights into dietary patterns and symptoms research, allowing for the definition and estimation of the prevalence of different groups of subjects characterized by different dietary choices and symptoms, and comparing those groups in relation to important health outcomes. At the same time, using the results of LCA analysis to establish a prediction model to screen high-risk individuals at the early stage of esophageal lesions, and to take intervention measures can effectively decrease the incidence and mortality of ESCC. Furthermore, cohort studies are needed to verify and expand our understanding.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eESCC: esophageal squamous cell carcinoma\u003c/p\u003e\n\u003cp\u003eLCA: latent class analysis\u003c/p\u003e\n\u003cp\u003eORs: odds ratio\u003c/p\u003e\n\u003cp\u003eCIs: confidence intervals\u003c/p\u003e\n\u003cp\u003eEADC: Esophageal adenocarcinoma\u003c/p\u003e\n\u003cp\u003eEPL: esophageal precancerous lesions\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLGIN: low-grade intraepithelial neoplasia\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHGIN: high-grade intraepithelial neoplasia\u003c/p\u003e\n\u003cp\u003ePCA: principal component analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFA: factor analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eESSH: esophageal squamous simple hyperplasia\u003c/p\u003e\n\u003cp\u003eESD: esophageal squamous dysplasia\u003c/p\u003e\n\u003cp\u003eAIC: Akaike information criterion\u003c/p\u003e\n\u003cp\u003eBIC: Bayesian information criterion\u003c/p\u003e\n\u003cp\u003eBMI: body mass index\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003ch2\u003eEthics statement\u003c/h2\u003e\n\u003cp\u003eThe study was approved by the independent ethics committee of National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (approval no. 17-124/1380) and ethics committee of Capital Medical University (approval no. Z2019SY012). All participants signed informed consent. All the experiment protocol for involving humans was in accordance to guidelines of Declaration of Helsinki.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the National Key R\u0026amp;D Program of China (grant numbers 2016YFC1302804, 2016YFC1302800, 2016YFC0901400); National Natural Science Foundation of China (grant numbers 81874277, 81974493); CAMS Innovation Fund for Medical Sciences (grant numbers 2016-I2M-1-001, 2017-I2M-1-001, 2019-I2M-2-004); and Sanming Project of Medicine in Shenzhen (grant number SZSM201911008).\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026apos; contributions\u003c/h2\u003e\n\u003cp\u003eW GQ, LF, Y AM and G XH designed this study. LY, W JL, L YQ, Z SK, Z YZ, Z LW, Z DL, L FG, C LN, W XZ, Z CL, S GH, Z ZY, L YP, YZ, W YX and G YY contributed to the data collection and organized the database. Z ZP, NC and FW analyzed the data. Z ZP draft the manuscript. LF, RN, Shen Y and Shao Y revised the manuscript. W GQ, LF and Y AM made the decision to submit for publication.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F: \u003cb\u003eGlobal Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries\u003c/b\u003e. \u003cem\u003eCA Cancer J Clin\u003c/em\u003e 2021, \u003cb\u003e71\u003c/b\u003e(3):209\u0026ndash;249.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen W, Zheng R, Baade PD, Zhang S, Zeng H, Bray F, Jemal A, Yu XQ, He J: \u003cb\u003eCancer statistics in China, 2015\u003c/b\u003e. \u003cem\u003eCA Cancer J Clin\u003c/em\u003e 2016, \u003cb\u003e66\u003c/b\u003e(2):115\u0026ndash;132.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbnet CC, Arnold M, Wei WQ: \u003cb\u003eEpidemiology of 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2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDar NA, Islami F, Bhat GA, Shah IA, Makhdoomi MA, Iqbal B, Rafiq R, Lone MM, Abnet CC, Boffetta P: \u003cb\u003ePoor oral hygiene and risk of esophageal squamous cell carcinoma in Kashmir\u003c/b\u003e. \u003cem\u003eBr J Cancer\u003c/em\u003e 2013, \u003cb\u003e109\u003c/b\u003e(5):1367\u0026ndash;1372.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbnet CC, Kamangar F, Islami F, Nasrollahzadeh D, Brennan P, Aghcheli K, Merat S, Pourshams A, Marjani HA, Ebadati A \u003cem\u003eet al\u003c/em\u003e: \u003cb\u003eTooth loss and lack of regular oral hygiene are associated with higher risk of esophageal squamous cell carcinoma\u003c/b\u003e. \u003cem\u003eCancer Epidemiol Biomarkers Prev\u003c/em\u003e 2008, \u003cb\u003e17\u003c/b\u003e(11):3062\u0026ndash;3068.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang X, Zhou X, Li Y, Tian X, Wang Y, Huang M, Ren L, Zhou L, Ding Z, Zhu J \u003cem\u003eet al\u003c/em\u003e: \u003cb\u003eA Novel Nomogram and Risk Classification System Predicting the Cancer-Specific Survival of Patients with Initially Diagnosed Metastatic Esophageal Cancer: A SEER-Based Study\u003c/b\u003e. \u003cem\u003eANN SURG ONCOL\u003c/em\u003e 2019, \u003cb\u003e26\u003c/b\u003e(2):321\u0026ndash;328.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp style=\"text-align: center;\"\u003eTable 1\u0026nbsp;\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003eModel fit information for latent class analysis of dietary patterns\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSubtype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eEntropy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eLMR\u003c/p\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eALMR\u003c/p\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1-Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e403812.534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e403930.899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2-Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e386659.699\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e386904.886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3-Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e380960.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e381332.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4-Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e377988.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e378486.881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5-Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e376775.273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e377400.920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6-Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e376013.990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e376766.458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0506\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e7-Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e375382.845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e376262.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\"\u003e\n \u003cp\u003eEsophagitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1-Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e383546.414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e383664.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2-Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e367382.696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e367626.386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3-Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e361925.857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e362295.594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4-Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e359187.987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e359683.771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5-Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e358056.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e358677.925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6-Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e357318.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e358066.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\"\u003e\n \u003cp\u003eLGIN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1-Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e354511.583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e354628.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2-Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e339894.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e340135.524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3-Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e335082.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e335448.584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4-Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e332591.286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e333082.474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.662\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5-Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e331522.734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e332138.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6-Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e330813.727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e331554.671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0674\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e7-Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e330280.873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e331146.696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.7604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.7604\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\"\u003e\n \u003cp\u003eHGIN and ESCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1-Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e341936.596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e342052.644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2-Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e327933.965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e328174.349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3-Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e323291.949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e323656.671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4-Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e321000.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e321489.221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5-Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e319970.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e320583.552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6-Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e319270.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e320007.879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0696\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e7-Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e318754.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e319616.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.5446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.5464\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: LGIN: low-grade intraepithelial neoplasia; HGIN: high-grade intraepithelial neoplasia; ESCC: esophageal squamous cell carcinoma; AIC: akaike information criterion; BIC: bayesian information criterion;\u0026nbsp;LMR:\u0026nbsp;Lo\u0026ndash;Mendell\u0026ndash;Rubin likelihood ratio test; ALMR: Lo\u0026ndash;Mendell\u0026ndash;Rubin Adjusted LMR test\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003eTable 2\u0026nbsp;\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003eModel fit information for latent class analysis of symptom classes\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSubtype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eEntropy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eLMR\u003c/p\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eALMR\u003c/p\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1-Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e136196.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e136255.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2-Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e134911.554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e135038.375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3-Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e134613.394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e134807.852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4-Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e134595.780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e134857.876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.1470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.1508\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003eEsophagitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1-Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e129391.888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e129450.710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2-Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e128168.972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e128295.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3-Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e127893.905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e128087.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4-Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e127881.486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e128141.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.5484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.5522\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003eLGIN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1-Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e120040.246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e120098.523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2-Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e118822.702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e118947.580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3-Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e118587.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e118778.779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4-Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e118569.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e118827.393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0778\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003eHGIN and ESCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1-Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e115838.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e115896.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2-Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e114651.925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e114776.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3-Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e114426.629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e114617.279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4-Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e114414.785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e114671.748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.1674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.1721\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: LGIN: low-grade intraepithelial neoplasia; HGIN: high-grade intraepithelial neoplasia; ESCC: esophageal squamous cell carcinoma; AIC: akaike information criterion; BIC: bayesian information criterion;\u0026nbsp;LMR:\u0026nbsp;Lo\u0026ndash;Mendell\u0026ndash;Rubin likelihood ratio test; ALMR: Lo\u0026ndash;Mendell\u0026ndash;Rubin Adjusted LMR test\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003eTable 3\u0026nbsp;\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003eProbabilities of consumption for selected food items by dietary patterns derived from LCA for all participants\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHealthy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eWestern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eLower consumers-\u003c/p\u003e\n \u003cp\u003ecombination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMedium consumers-\u003c/p\u003e\n \u003cp\u003ecombination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHigher consumers-\u003c/p\u003e\n \u003cp\u003ecombination\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003eLivestock meat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eeveryday\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e77.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e84.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e14.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e9.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e54.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1-6days/week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e12.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e12.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e22.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e72.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e43.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;1day/week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e10.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e62.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e18.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003ePoultry\u0026nbsp;meat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ge;1day/week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e7.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e7.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e89.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;1day/week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e93.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e92.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e98.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e96.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e10.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eSeafood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ge;1day/week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e9.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e64.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;1day/week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e98.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e90.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e98.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e99.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e35.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003eEggs and their products\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eeveryday\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e61.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e73.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e22.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e31.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e50.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1-6days/week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e15.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e16.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e24.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e64.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e38.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;1day/week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e23.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e10.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e53.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e11.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003eVegetables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eeveryday\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e99.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e97.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1-6days/week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e97.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;1day/week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e99.4%%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e94.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003eFruits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eeveryday\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e41.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e18.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e31.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1-6days/week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e21.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e41.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e11.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e59.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e54.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;1day/week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e37.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e40.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e85.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e37.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e13.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eBean\u0026nbsp;products\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ge;1day/week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e38.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e46.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e8.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e45.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e65.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;1day/week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e62.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e53.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e91.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e54.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e34.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eScallion,\u0026nbsp;ginger\u0026nbsp;and\u0026nbsp;garlic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ge;1day/week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e93.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e72.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e36.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e59.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e84.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;1day/week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e27.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e63.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e40.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e15.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003ePickles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ge;1day/week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e68.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e11.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e17.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;1day/week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e95.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e31.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e96.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e88.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e82.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eNut\u0026nbsp;fruits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ge;1day/week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e16.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e33.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e24.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;1day/week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e83.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e66.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e97.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e95.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e75.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eCluster\u0026rsquo;s size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e28.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e9.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e29.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e28.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003eTable 4\u0026nbsp;\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003eProbabilities of consumption for selected symptom items by severity of symptom derived from LCA for all participants\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAsymptomatic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMild symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eOvert\u0026nbsp;symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003eNumber of lost teeth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0 teeth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e48.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e39.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e32.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1-3 teeth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e29.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e39.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e31.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003emore than 4 teeth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e22.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e21.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e35.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eWhether\u0026nbsp;gingival bleeding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e64.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e35.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eWhether dysphagia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e7.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e99.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e99.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e93.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eWhether bloating, heartburn, acid reflux\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e47.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e98.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e98.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e52.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eWhether nausea, vomiting and belching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e36.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e99.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e99.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e63.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eWhether epigastric pain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e20.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e99.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e99.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e79.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eCluster\u0026rsquo;s size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e71.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e26.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003eTable 5\u0026nbsp;\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003eDietary patterns\u0026rsquo; characteristics according to selected sociodemographic variables for all participants\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHealthy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eWestern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eLower consumers-\u003c/p\u003e\n \u003cp\u003ecombination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMedium consumers-\u003c/p\u003e\n \u003cp\u003ecombination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHigher consumers-\u003c/p\u003e\n \u003cp\u003ecombination\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNormal control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e89.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e84.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e79.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e82.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e84.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eEsophagitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e9.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e14.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e12.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e10.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLGIN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHGIN and ESCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e33.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e28.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e27.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e33.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e33.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e51-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e44.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e45.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e42.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e41.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e41.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026gt;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e22.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e26.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e30.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e25.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e24.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e41.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e46.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e40.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e38.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e46.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e58.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e53.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e60.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e61.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e54.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNever been to school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e14.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e13.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e16.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e14.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e12.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePrimary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e34.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e33.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e39.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e35.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e30.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMiddle school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e39.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e43.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e35.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e38.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e42.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHigh school and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e11.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e9.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e8.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e11.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e15.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e18.6-23.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e46.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e44.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e48.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e44.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e48.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e24.0-26.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e39.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e40.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e39.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e40.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e39.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026gt;27.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e11.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e13.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e9.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e12.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e10.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003eSmoking status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNever smoked\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e74.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e64.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e77.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e77.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e73.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eCurrent smoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e20.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e27.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e19.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e18.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e22.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePrevious smoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e8.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003eDrinking status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNo alcohol consumption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e62.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e57.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e75.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e63.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e64.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;1 time/week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e28.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e20.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e19.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e29.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e20.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026ge;1 time/week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e9.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e22.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4.95%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e7.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e15.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: LGIN: low-grade intraepithelial neoplasia; HGIN: high-grade intraepithelial neoplasia; ESCC: esophageal squamous cell carcinoma; BMI: body mass index.\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003eTable 6\u0026nbsp;\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003eSeverity of symptoms\u0026rsquo; characteristics according to selected sociodemographic variables for all participants\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAsymptomatic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMild symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eOvert\u0026nbsp;symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNormal control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e84.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e82.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e89.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eEsophagitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e11.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e11.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLGIN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHGIN and ESCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e30.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e33.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e25.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e51-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e43.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e43.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e40.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026gt;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e26.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e23.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e33.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e41.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e38.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e36.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e58.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e61.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e63.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNever been to school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e15.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e13.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e25.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePrimary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e35.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e38.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e33.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMiddle school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e39.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e38.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e29.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHigh school and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e11.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e9.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e11.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e18.6-23.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e46.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e47.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e51.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e24.0-26.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e40.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e39.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e36.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026gt;27.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e12.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e10.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e8.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003eSmoking status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNever smoked\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e74.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e78.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e74.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eCurrent smoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e21.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e17.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e19.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePrevious smoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eDrinking status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNo alcohol consumption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e65.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e67.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e70.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;1 time/week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e24.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e26.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e25.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026ge;1 time/week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e9.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: LGIN: low-grade intraepithelial neoplasia; HGIN: high-grade intraepithelial neoplasia; ESCC: esophageal squamous cell carcinoma; BMI: body mass index.\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003eTable 7\u0026nbsp;\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003eOdds ratios (OR) and the corresponding 95% confidence intervals (CIs) of esophageal cancer for each cluster of dietary patterns adjusting for known confounders: per disease stage\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\"\u003e\n \u003cp\u003eAdjusted OR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\"\u003e\n \u003cp\u003eDisease stage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eEsophagitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eLGIN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHGIN and ESCC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eCase/Control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3883/29079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1413/29079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e332/29079\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\"\u003e\n \u003cp\u003eDietary Patterns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHealthy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eWestern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.42(1.23-1.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.54(1.27-1.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.69(1.14-2.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eLower consumers-combination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.33(2.11-2.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.39(1.20-1.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.72(1.26-2.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMedium consumers-combination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.99(1.80-2.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.30(1.12-1.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.64(1.19-2.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHigher consumers-combination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.59(1.32-1.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.44(1.09-1.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.62(0.92-2.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: LGIN: low-grade intraepithelial neoplasia; HGIN: high-grade intraepithelial neoplasia; ESCC: esophageal squamous cell carcinoma.\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003eTable 8\u0026nbsp;\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003eOdds ratios (OR) and the corresponding 95% confidence intervals (CIs) of esophageal cancer for each cluster of symptom clusters adjusting for known confounders: per disease stage\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\"\u003e\n \u003cp\u003eAdjusted OR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\"\u003e\n \u003cp\u003eDisease stage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eEsophagitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eLGIN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHGIN and ESCC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eCase/Control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3883/29079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1413/29079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e332/29079\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003eSeverity of symptom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAsymptomatic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMild symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.87(1.66-2.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.25(1.11-1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.98(0.58-1.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eOvert\u0026nbsp;symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.04(0.86-1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.82(0.51-1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.58(1.00-2.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: LGIN: low-grade intraepithelial neoplasia; HGIN: high-grade intraepithelial neoplasia; ESCC: esophageal squamous cell carcinoma.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Esophageal squamous cell carcinoma, Precancerous lesions, Latent class analysis, Dietary patterns, Symptom","lastPublishedDoi":"10.21203/rs.3.rs-1038856/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1038856/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eDietary patterns and symptoms research among Chinese with esophageal squamous cell carcinoma (ESCC) and its precursor lesions is limited, especially as it relates to multiple food consumption and multiple co-occurring symptoms. The aim of our study was to identify the dietary patterns and severity of symptom classes with the risk of esophageal squamous cell carcinoma and its histological precursor lesions, and develop a risk prediction model for different stages of esophageal disease.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe analyzed data from a multicenter cross-sectional study carried out in ESCC high incidence areas between 2017 and 2018, which included 34,707 individuals aged 40-69 years. Dietary patterns and severity of symptom classes were derived by applying a latent class analysis (LCA). A multiple logistic regression model was used to derive the odds ratio (ORs) and corresponding 95% confidence intervals (CIs) for ESCC and the different stages of esophageal disease according to the dietary patterns and severity of symptom classes identified. We built the risk prediction model by using a nomogram.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eWe identified five dietary patterns and three severity of symptom classes. The dietary patterns were classified as follows: “Healthy”, “Western”, “Lower consumers-combination”, “Medium consumers-combination” and “Higher consumers-combination” patterns based on the intake of foods such as red meat, vegetables and fruits. The severity of symptoms was categorized into “Asymptomatic”, “Mild symptoms” and “Overt\u0026nbsp;symptoms” classes based on health-related symptoms reported by the participants. Compared to the “Healthy” pattern, the other four patterns were all associated with an increased risk of esophageal disease. Similarly, the other two symptom classes present different degrees of increased risk of esophageal disease compared to the “Asymptomatic”. The nomograms reflect the good predictive ability of the model.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eAmong individuals aged 40-69 years in high incidence regions of upper gastrointestinal cancer, the results supplied important epidemiological evidence and given further insights into dietary patterns and symptoms research.\u003c/p\u003e","manuscriptTitle":"Dietary Patterns and Severity of Symptom with the Risk of Esophageal Squamous Cell Carcinoma and its Histological Precursor Lesions in China: A Multicenter Cross-Sectional Latent Class Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-11-10 16:18:49","doi":"10.21203/rs.3.rs-1038856/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2021-12-03T11:22:11+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-11-15T21:52:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3c91a664-fc5c-4710-8629-aff30a163931","date":"2021-11-13T14:02:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"97f7155e-f1e7-479c-8ad8-1017db315569","date":"2021-11-13T02:43:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-11-13T02:37:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-11-13T02:34:01+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2021-11-08T04:59:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2021-11-08T04:56:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2021-11-01T09:18:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f6e273d3-6f88-4bcb-ab74-12a07e5e83ec","owner":[],"postedDate":"November 10th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":8418292,"name":"Cancer Biology"}],"tags":[],"updatedAt":"2022-01-18T05:14:07+00:00","versionOfRecord":[],"versionCreatedAt":"2021-11-10 16:18:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1038856","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1038856","identity":"rs-1038856","version":["v1"]},"buildId":"WvIrzKhiLBfengagbw6Ux","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}