Factors influencing the incidence of early gastric cancer: A Bayesian Network analysis

Factors influencing the incidence of early gastric cancer: A Bayesian Network 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 Factors influencing the incidence of early gastric cancer: A Bayesian Network analysis Ruiyu Li, Taiming Yang, Zi Dong, Yin Gao, Nan Li, Ting Song, Jinshu Sun, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5340183/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Mar, 2025 Read the published version in BMC Gastroenterology → Version 1 posted 9 You are reading this latest preprint version Abstract Background This study aims to establish a Bayesian network risk prediction model for gastric cancer using data mining methods. It explores both direct and indirect factors influencing the incidence of gastric cancer and reveals the interrelationships among these factors. Methods Data were collected from early cancer screenings conducted at the People's Hospital of Lincang between 2022 and 2023. A Lasso regression model was utilized for preliminary variable selection, and the Bayesian network model was constructed using R software. The network structure analysis was visualized with Netica software, followed by inference and evaluation of the model. Results The incidence rate of gastric cancer in this region high-risk population was determined to be 7.09%. The Lasso regression model identified several risk factors for gastric cancer, including ethnicity, upper gastrointestinal symptoms (nausea, acid reflux, vomiting), alcohol consumption, severe gastric intestinal metaplasia, and a family history of upper gastrointestinal cancers. A total of seven risk factors were incorporated into the Bayesian network model, resulting in a network structure consisting of eight nodes and twelve edges. The area under the curve (AUC) for the network model was found to be 0.615. Conclusion The Bayesian network model provides an intuitive framework for understanding the direct and indirect factors contributing to the early onset of gastric cancer, elucidating the interrelationships among these factors. Furthermore, the model demonstrates satisfactory predictive performance, which may facilitate the early detection of gastric cancer and enhance the levels of early diagnosis and treatment among high-risk populations. Gastric cancer Bayesian networks Machine learning Lasso regression Figures Figure 1 Figure 2 Figure 3 Introduction Gastric cancer (GC) is a malignant tumor characterized by the abnormal proliferation of epithelial cells in the gastric mucosa, making it one of the most prevalent malignancies worldwide and posing a significant threat to human health [1, 2] . According to the latest report from the International Agency for Research on Cancer (IARC), there were approximately 19.96 million new cancer cases and 9.74 million cancer-related deaths globally in 2022. Among these, the number of new gastric cancer cases was 966,000, with nearly 660,000 deaths, ranking fifth in both incidence and mortality worldwide [2] . In China, there were 358,700 new gastric cancer cases and 260,400 deaths, positioning its incidence as the fifth highest among all cancers and its mortality as the third highest [3] . Although the incidence and mortality rates of gastric cancer in China have exhibited a downward trend from 1990 to 2019, in 2022, the country accounted for 37.0% of the world's new gastric cancer cases and 39.4% of gastric cancer deaths, indicating that the overall burden of incidence and mortality remains substantial [4, 5] . Gastric cancer has emerged as a major public health challenge in China. The five-year survival rate for patients with early-stage gastric cancer can reach as high as 85% [6] . However, due to the subtle nature of early symptoms, most patients are diagnosed at more advanced stages [7] . Even with prompt treatment at this stage, the five-year survival rate declines to below 10% [6] . Consequently, the implementation of gastric cancer screening is essential for improving prognosis and enhancing patient survival rates [8] . Endoscopic examination combined with pathological tissue biopsy is regarded as the "gold standard" for early gastric cancer screening [9–11] . However, imaging and endoscopic procedures often entail high costs and depend on advanced equipment and physician expertise, rendering them difficult to implement in rural areas and regions with limited medical resources [12] . Furthermore, the discomfort associated with endoscopic procedures leads to low acceptance among asymptomatic patients, impeding widespread application [13] . In China, gastric cancer screening primarily targets symptomatic patients and relies on opportunistic endoscopic screenings. The uneven socioeconomic development and healthcare resource allocation across various regions in China have posed significant challenges to the comprehensive implementation of endoscopic screening within the population [14] . Given the low compliance and accessibility of endoscopic screening, investigating the factors influencing the incidence of early gastric cancer and developing risk models can offer valuable support for early intervention strategies. The development of gastric cancer is influenced by various risk factors [2, 8] . Machine learning methods can intelligently and efficiently identify these risk factors within populations. The Least Absolute Shrinkage and Selection Operator (Lasso) regression [15] effectively addresses multicollinearity issues in high-dimensional data, thereby facilitating the identification of significant factors influencing gastric cancer incidence. Bayesian Networks (BN) serve as a valuable machine learning tool for risk prediction in medical science, allowing for more precise risk stratification compared to traditional models. They also display the network structure of variable relationships and enable the construction of personalized risk prediction models [16, 17] . Lincang City, situated in the southwestern region of Yunnan Province, is characterized by its diverse population, which includes 23 ethnic minorities. In recent years, there has been a notable increase in both the incidence and mortality rates of malignant tumors in this city. According to tumor monitoring data from 2019, Lincang City reported 3,080 new cases of malignant tumors, resulting in an incidence rate of 181.71 per 100,000 individuals. Furthermore, there were 1,447 deaths attributed to malignant tumors, yielding a mortality rate of 85.37 per 100,000 individuals [18] . Importantly, the incidence rate of gastric cancer in Lincang City was found to be 1.62 times higher than the average incidence rate in Yunnan Province, while the mortality rate was 1.80 times higher than the provincial average [19] . Consequently, addressing the rising incidence and mortality rates of gastrointestinal malignant tumors, particularly gastric cancer, has emerged as a pressing public health challenge that requires immediate attention in Lincang City. This study employs Lasso regression and Bayesian network predictive models to assess and predict the risk of gastric cancer, aiming to provide more accurate support for clinical early diagnosis and prevention. Methods Study design and settings We conducted a cross-sectional study with 2022–2023 in the People's Hospital of Lincang to screen for diseases and questionnaires among people at high risk of gastric cancer. The definition of people at high risk of gastric cancer was referred to the Gastric Cancer Screening and Early Diagnosis and Treatment Programme (2024 Edition) (hereinafter referred to as the Programme) issued by the National Health Commission of the PRC [20] . The screening was performed in accordance with the objectives and methodology provided for in the programme. Initially, we included all eligible high-risk groups while excluding those who were younger than 40 years of age, non-residents of Lincang city ( < = 2 years of local residence), and serious mental or physical illnesses or unwillingness to cooperate. This study was approved by the Ethics Committee of Kunming Medical University (Approval No. KMMU2024MEC021) prior to commencement, and informed consent was obtained from all participants. Study samples A total of 1820 gastric cancer high-risk individuals were included in this study, and were divided into gastric cancer group (n = 129) and non-gastric cancer group (n = 1691) according to whether they developed gastric cancer or not. Gastroscopy results in the Guidelines for the Diagnosis and Treatment of Gastric Cancer (2022 Edition) [21] were utilized to determine whether the patient had developed gastric cancer. Measures We developed a questionnaire for the assessment of people at risk of upper gastrointestinal cancer in accordance with the Programme. The questionnaire included four main domains: general characteristics (gender, age, ethnicity and current address), upper gastrointestinal symptoms (bloating, heartburn, acid reflux, nausea, hiccup, belching, eating discomfort, upper abdominal pain, and vomiting), risk factors for upper gastrointestinal cancers (smoking, alcohol consumption, scalding hot food, overspeed eating, indoor air pollution, toothlessness, pernicious anemia), precancerous diseases or lesions (gastric severe intestinal epithelial hyperplasia, giant folds of gastric mucosa sign, 10 years of postoperative gastric remnants after surgery for benign disease, more than 6 years of postoperative residual stomach after gastric, low-grade intraepithelial neoplasia, family history of upper gastrointestinal tract cancer, stomach polyp, types of gastritis, gastric ulcer) and gastroscopy for gastric tumors. Statistical analysis All analyses were undertaken using R4.3.3 and its installation package and Netical software. Firstly, “glment” package was used for Lasso regression to screen for variables that have a significant impact on gastric cancer. Lasso [15] , based on the least squares method, allows for the shrinkage of less significant variable coefficients to zero while retaining the coefficients of important variables. Lasso regression effectively addresses multicollinearity issues among variables and identifies those of notable significance. Secondly, to develop our model, we split data into a 70% training dataset using the “caret” package and a 30% test dataset. Subsequently, in the training dataset, we employed the variables screened by lasso regression to construct a Bayesian network model with “bnlearn” package, the hill-climbing algorithm (hc) for structural learning, and great likelihood estimation to learn the parameters. Bayesian Networks (BN) are models that simulate the uncertainty of causal relationships during reasoning processes [22, 23] . The model primarily consists of a directed acyclic graph and a collection of conditional probability tables. In this framework, nodes represent variables, with each node corresponding to a specific variable. Directed arcs connecting two nodes indicate direct probabilistic dependencies, while conditional probabilities express the strength of relationships among the nodes. Sub-nodes are those to which directed arcs point, while their parent nodes are those from which the arrows originate. The direction of the arrows qualitatively describes the relationships among the nodes. After the model was constructed, accuracy, area under the curve (AUC) and sensitivity and specificity were used to evaluate model discrimination. Finally, Netica software was applied to draw the topology of the Bayesian network and carry out Bayesian inference. Results Demographic characteristics A total of 1,820 eligible patients were included in the study, comprising 743 males (40.82%) and 1,077 females (59.18%), with a mean age of 56.22 ± 7.64 years. Among the participants, 129 cases of gastric cancer (7.09%) were identified, while 1,691 patients (92.91%) were diagnosed with non-gastric cancer. Table 1 General characteristics of patients [n (%)] GC patients (N = 129) Non-GC patients (N = 1691) Overall (N = 1820) Gender Male 57 (44.2) 686 (40.6) 743 (40.8) Female 72 (55.8) 1005 (59.4) 1077 (59.2) Age ≥ 55 56 (43.4) 665 (39.3) 721 (39.6) <55 73 (56.6) 1026 (60.7) 1099 (60.4) Ethnicity Han ethnic group 74 (57.4) 762 (45.1) 836 (45.9) Wa ethnic group 28 (21.7) 310 (18.3) 338 (18.6) Dai ethnic group 7 (5.4) 235 (13.9) 242 (13.3) Yi ethnic group 13 (10.1) 148 (8.8) 161 (8.8) Other ethnic groups 7 (5.4) 236 (14.0) 243 (13.4) Current address Zhenkang, Gengma and Cangyuan county 50 (38.8) 704 (41.6) 754 (41.4) Shuangjiang, Linxiang and Yunxian county 48 (37.2) 737 (43.6) 785 (43.1) Fengqing and Yongde county 31 (24.0) 250 (14.8) 281 (15.4) Bloating No 69 (53.5) 951 (56.2) 1020 (56.0) Yes 60 (46.5) 740 (43.8) 800 (44.0) Heartburn No 103 (79.8) 1453 (85.9) 1556 (85.5) Yes 26 (20.2) 238 (14.1) 264 (14.5) Acid reflux No 93 (72.1) 1365 (80.7) 1458 (80.1) Yes 36 (27.9) 326 (19.3) 362 (19.9) Nausea No 103 (79.8) 1491 (88.2) 1594 (87.6) Yes 26 (20.2) 200 (11.8) 226 (12.4) Hiccup No 119 (92.2) 1579 (93.4) 1698 (93.3) Yes 10 (7.8) 112 (6.6) 122 (6.7) Belching No 112 (86.8) 1551 (91.7) 1663 (91.4) Yes 17 (13.2) 140 (8.3) 157 (8.6) Eating discomfort No 104 (80.6) 1462 (86.5) 1566 (86.0) Yes 25 (19.4) 229 (13.5) 254 (14.0) Upper abdominal pain No 53 (41.1) 596 (35.2) 649 (35.7) Yes 76 (58.9) 1095 (64.8) 1171 (64.3) Vomiting No 119 (92.2) 1636 (96.7) 1755 (96.4) Yes 10 (7.8) 55 (3.3) 65 (3.6) Smoking No 103 (79.8) 1336 (79.0) 1439 (79.1) Yes 26 (20.2) 355 (21.0) 381 (20.9) Drinking No 124 (96.1) 1531 (90.5) 1655 (90.9) Yes 5 (3.9) 160 (9.5) 165 (9.1) Scalding hot food No 109 (84.5) 1427 (84.4) 1536 (84.4) Yes 20 (15.5) 264 (15.6) 284 (15.6) Overspeed eating No 100 (77.5) 1268 (75.0) 1368 (75.2) Yes 29 (22.5) 423 (25.0) 452 (24.8) Indoor air pollution No 129 (100) 1687 (99.8) 1816 (99.8) Yes 0 (0) 4 (0.2) 4 (0.2) Toothlessness No 65 (50.4) 851 (50.3) 916 (50.3) Yes 64 (49.6) 840 (49.7) 904 (49.7) Pernicious anemia No 127 (98.4) 1683 (99.5) 1810 (99.5) Yes 2 (1.6) 8 (0.5) 10 (0.5) Gastric severe intestinal epithelial hyperplasia No 127 (98.4) 1688 (99.8) 1815 (99.7) Yes 2 (1.6) 3 (0.2) 5 (0.3) Giant folds of gastric mucosa sign No 128 (99.2) 1690 (99.9) 1818 (99.9) Yes 1 (0.8) 1 (0.1) 2 (0.1) 10 years of postoperative gastric remnants after surgery for benign disease No 128 (99.2) 1690 (99.9) 1818 (99.9) Yes 1 (0.8) 1 (0.1) 2 (0.1) More than 6 years of postoperative residual stomach after gastric cancer No 127 (98.4) 1686 (99.7) 1813 (99.6) Yes 2 (1.6) 5 (0.3) 7 (0.4) Low-grade intraepithelial neoplasia No 129 (100) 1689 (99.9) 1818 (99.9) Yes 0 (0) 2 (0.1) 2 (0.1) Family history of upper gastrointestinal tract cancer No 121 (93.8) 1677 (99.2) 1798 (98.8) Yes 8 (6.2) 14 (0.8) 22 (1.2) Cardia polyp No 128 (99.2) 1669 (98.7) 1797 (98.7) Yes 1 (0.8) 22 (1.3) 23 (1.3) Stomach polyp No 91 (70.5) 1214 (71.8) 1305 (71.7) Yes 38 (29.5) 477 (28.2) 515 (28.3) Types of gastritis Atrophic 13 (10.1) 230 (13.6) 243 (13.4) Non-atrophic 99 (76.7) 1128 (66.7) 1227 (67.4) Dissipated 3 (2.3) 33 (2.0) 36 (2.0) Hybrid type 7 (5.4) 281 (16.6) 288 (15.8) None 7 (5.4) 19 (1.1) 26 (1.4) Gastric ulcer No 123 (95.3) 1627 (96.2) 1750 (96.2) Yes 6 (4.7) 64 (3.8) 70 (3.8) Variable Selection Using Lasso Regression A total of 30 influencing factors were included in the Lasso regression analysis. Through five-fold cross-validation, seven clinically significant indicators were identified: ethnicity, upper gastrointestinal symptoms (nausea, acid reflux, vomiting), alcohol consumption, severe gastric intestinal metaplasia, and family history of upper gastrointestinal cancer (Fig. 1 ). Construction of the Bayesian Network Model Using the seven variables selected from the Lasso regression model, a Bayesian network predictive model was constructed to explore the relationships between gastric cancer and its associated influencing factors, employing the hc algorithm. As illustrated in Fig. 2 , the resulting network model comprised eight nodes and twelve edges. The analysis revealed that alcohol consumption, family history of upper gastrointestinal cancer, severe gastric intestinal metaplasia, vomiting, nausea, and acid reflux are directly associated with the occurrence of gastric cancer, while ethnicity is indirectly related through alcohol consumption. Inference of the Bayesian Network Model The constructed network model was employed for predictive assessments, utilizing relevant node information to evaluate the risk of gastric cancer in patients. For instance, when a patient has a history of alcohol consumption but exhibits no other disease symptoms, regardless of ethnicity, Bayesian network inference indicates that the likelihood of developing gastric cancer decreases to 7.35% (Fig. 3 A). Conversely, when a patient is diagnosed with severe gastric intestinal metaplasia and presents with symptoms of nausea and acid reflux, the risk of developing gastric cancer escalates to 55.2% (Fig. 3 B). Evaluation of the Bayesian Network Model The results showed that 505 patients were correctly classified, yielding a classification accuracy of 92.66%, a sensitivity of 15.79%, and a specificity of 98.22%. The area under the curve (AUC) for the model evaluated on the test set was 0.615, indicating that the constructed Bayesian network model performed effectively and accurately represented the relationships among the various nodes. Discussion With the aging population and the rising prevalence of unhealthy lifestyles, the burden of gastric cancer in China is gradually increasing [2] . The occurrence and progression of gastric cancer are influenced by multiple factors; identifying these direct and indirect influencing factors can facilitate the early identification of high-risk populations, improve the levels of early diagnosis and treatment [1] , and mitigate the loss of quality of life associated with gastric cancer. Consequently, employing a Bayesian network to construct a diagram of the influencing factors elucidates the complex relationships among them, thereby contributing positively to the reduction of gastric cancer incidence. The findings of this study indicate that alcohol consumption, ethnicity, family history of upper gastrointestinal cancer, vomiting, acid reflux, nausea, and severe gastric intestinal metaplasia are significant influencing factors for the occurrence of gastric cancer. Ethnicity is directly related to gastric cancer incidence. Lincang, a city located on the southwestern border of China, there is a notable concentration of ethnic minorities. The Wa, Dai, and Yi ethnic groups were the main ethnic minorities in the region. Unique customs and dietary practices, such as the consumption of raw meat and untreated water, may contribute to the elevated incidence of gastric cancer [24] , suggesting that ethnic areas are a part of the prevention and treatment of gastric cancer. Previous studies have reported that the risk of gastric cancer were higher among alcohol consumers compared to non-drinkers [8, 25] . However, our study revealed that alcohol consumption may actually reduce the risk of gastric cancer. A meta-analysis of cohort studies examining the relationship between alcohol consumption and gastric cancer risk indicated that light to moderate drinking does not significantly affect the risk of gastric cancer compared to non-drinkers [26] . This phenomenon may be attributed to the antibacterial effects of alcohol against Helicobacter pylori [27, 28] . Furthermore, some research has suggested that male light to moderate drinkers (1-5g/day) exhibit the lowest risk of alcohol-related cancer mortality [29] , implying that moderate alcohol consumption may have a protective effect against gastric cancer. Future studies should aim to further investigate the dose-response relationship between alcohol consumption and the occurrence of gastric cancer, as well as explore the underlying mechanisms involved. Moreover, alcohol has been identified as a risk factor for various cancers, including esophageal and liver cancer [30] , highlighting the necessity for further validation of this conclusion. Severe gastric intestinal metaplasia was recognized as a precancerous lesion for gastric cancer [31] . Over time, chronic atrophic gastritis can lead to the replacement of gastric mucosal epithelial cells with intestinal epithelial cells, facilitating the progression of precancerous lesions to the intestinal metaplasia stage. Early diagnosis of intestinal metaplasia, followed by appropriate medical intervention and endoscopic treatment, can alleviate clinical symptoms, enhance the physiological function of the gastric mucosa, and reduce or delay the onset of gastric cancer [32] . Acid reflux, vomiting, and nausea were common upper gastrointestinal symptoms associated with various gastrointestinal diseases and served as important predictive indicators for gastric cancer risk. These clinical manifestations may suggest a history of gastrointestinal disorders, such as intestinal metaplasia. A meta-analysis has indicated that the risk of gastric cancer among patients with gastrointestinal diseases and chronic gastrointestinal conditions is 4.85 times and 4.40 times greater, respectively, than that of the general population [33] . Therefore, when patients present with frequent upper gastrointestinal symptoms, further investigation, including gastroscopy and pathological biopsy, when necessary, should be conducted. Furthermore, in alignment with the findings of Zhang Linglin [34] and EOM [35] , a family history of upper gastrointestinal cancer is a well-established risk factor for gastric cancer. Studies indicate that 10%-15% of gastric cancer patients have a familial history of gastric tumors [36] , with a significantly elevated incidence observed among first-degree relatives of gastric cancer patients [37] . This increased risk may be attributed to shared genetic loci within families [38] . Limitations The limitations of this study include the fact that all samples were sourced from a single institution, which restricts the generalizability of the findings to the broader population. Consequently, future research should involve multiple institutions across diverse geographic regions to increase the sample size and obtain more relevant indicators for a comprehensive investigation. This approach would enhance the practical applicability and robustness of the model. Conclusion This study employed Lasso regression for variable selection, effectively addressing the issue of multicollinearity among variables and thereby identifying those of significant importance. Subsequently, a Bayesian network was utilized for structural and parameter learning, allowing for the calculation of the impact of various factors on the early risk of gastric cancer. This approach visually illustrates the direct and indirect relationships among the influencing factors, clarifying the internal regulatory mechanisms that govern these relationships. By overcoming the limitations of traditional predictive models in explaining causal relationships and probability calculations, this methodology aids healthcare professionals in the early identification of high-risk populations for gastric cancer. Furthermore, it enhances the levels of early diagnosis and treatment, facilitating timely targeted interventions to mitigate the risk of gastric cancer and reduce the associated loss of quality of life. Abbreviations GC gastric cancer Lasso Least Absolute Shrinkage and Selection Operator BN Bayesian Networks AUC area under the curve Declarations Acknowledgements We gratefully acknowledge the collaborative efforts of the study participants and data collectors in the designated survey area. Author contributions RYL, TMY and YC conceived the study and designed the protocol. ZD, YG and NL supervised the implementation of the study and advised on data analysis. RYL, TMY, TS and JSS were involved in the development of the questionnaires, data collection and analysis. RYL drafted the original manuscript. YC revised the original manuscript. All authors reviewed and approved the final manuscript. YC and RD are the sponsors of this study. Funding Role of funding source This study was supported by a grant from Yunnan Provincial Talent Program for Young Scholar and Technical Reserve Personnel (202305AC160046), National Key Scientific and Technological Project for Sustainable Development Demonstration Zones (202104AC100001-A11) Availability of data and materials The datasets used and/or analyzed in this study are available from the corresponding author on request. Ethics approval and consent to participate This study protocol was approved by the Ethics Committee of Kunming Medical University (Approval No. KMMU2024MEC021) prior to commencement. Adhering to the principle of voluntary participation, all potential participants were given the opportunity to make an informed decision on their participation in the study. All subjects were clearly informed of their right to withdraw from the study at any time without facing adverse consequences. To ensure transparency, the purpose and procedures of the study were fully explained to the subjects before signing the informed consents. Individually identifiable information, such as name and telephone number, was deliberately omitted from the recorded data during the data collection phase to ensure anonymity. Finally, Information collected was subjected to appropriate coding procedures and kept strictly confidential throughout the research process. 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Chinese Journal of Gastroenterology and Hepatology, 2014, 23(02): 143-6. YANG L, YUANYUAN N, XIANMEI M. Diagnosis and treatment of precancerous lesions of gastric cancer [J]. Journal of Digestive Oncology(Electronic Version), 2022, 14(02): 113-8. BIN Z, XUEQI F, XIAOLONG Z, et al. A case-control study on risk factors of gastric cancer in four provinces of Huaihe river basin [J]. Chinese Journal of Prevention and Control of Chronic Diseases, 2022, 30(06): 437-41+46.https://doi.org/10.16386/j.cjpccd.issn.1004-6194.2022.06.008 LINGLIN Z. Construction and Verification of Risk Prediction Model for Gastric Cancer [D]; Chengdu University of TCM, 2023. EOM B W, JOO J, KIM S, et al. Prediction Model for Gastric Cancer Incidence in Korean Population [J]. PLoS One, 2015, 10(7): e0132613.https://doi.org/10.1371/journal.pone.0132613 ZENGYUN W, JING N, YUE W, et al. Research progress on risk factors of gastric cancer in families [J]. China Medicine, 2023, 18(08): 1264-7. SHIN C M, KIM N, YANG H J, et al. Stomach cancer risk in gastric cancer relatives: interaction between Helicobacter pylori infection and family history of gastric cancer for the risk of stomach cancer [J]. J Clin Gastroenterol, 2010, 44(2): e34-9.https://doi.org/10.1097/MCG.0b013e3181a159c4 CHEN B, WANG Y, TANG W, et al. Association between PPARgamma, PPARGC1A, and PPARGC1B genetic variants and susceptibility of gastric cancer in an Eastern Chinese population [J]. BMC Med Genomics, 2022, 15(1): 274.https://doi.org/10.1186/s12920-022-01428-0 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 21 Mar, 2025 Read the published version in BMC Gastroenterology → Version 1 posted Editorial decision: Revision requested 06 Jan, 2025 Reviews received at journal 24 Dec, 2024 Reviews received at journal 14 Dec, 2024 Reviewers agreed at journal 13 Dec, 2024 Reviewers agreed at journal 12 Dec, 2024 Reviewers invited by journal 18 Nov, 2024 Editor assigned by journal 14 Nov, 2024 Submission checks completed at journal 29 Oct, 2024 First submitted to journal 27 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5340183","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":371534261,"identity":"d40c0e60-9393-4163-893d-0d544e85afa9","order_by":0,"name":"Ruiyu Li","email":"","orcid":"","institution":"School of Public Health, Kunming Medical University, Kunming, Yunnan","correspondingAuthor":false,"prefix":"","firstName":"Ruiyu","middleName":"","lastName":"Li","suffix":""},{"id":371534262,"identity":"90151abc-9886-4020-b019-08301fbfef72","order_by":1,"name":"Taiming Yang","email":"","orcid":"","institution":"Department of Gastroenterology, The People's Hospital of Lincang, Lincang, Yunnan","correspondingAuthor":false,"prefix":"","firstName":"Taiming","middleName":"","lastName":"Yang","suffix":""},{"id":371534263,"identity":"9da8acee-c72f-4e94-9126-b4146d4214f2","order_by":2,"name":"Zi Dong","email":"","orcid":"","institution":"School of Public Health, Kunming Medical University, Kunming, Yunnan","correspondingAuthor":false,"prefix":"","firstName":"Zi","middleName":"","lastName":"Dong","suffix":""},{"id":371534264,"identity":"c5070d2a-bb42-4dac-9533-5400ba7ee7c7","order_by":3,"name":"Yin Gao","email":"","orcid":"","institution":"School of Public Health, Kunming Medical University, Kunming, Yunnan","correspondingAuthor":false,"prefix":"","firstName":"Yin","middleName":"","lastName":"Gao","suffix":""},{"id":371534265,"identity":"d157ea33-8fc6-4b13-a9fa-e8b2df54dc92","order_by":4,"name":"Nan Li","email":"","orcid":"","institution":"School of Public Health, Kunming Medical University, Kunming, Yunnan","correspondingAuthor":false,"prefix":"","firstName":"Nan","middleName":"","lastName":"Li","suffix":""},{"id":371534266,"identity":"520e7a76-b8ce-4e08-bacf-85287c1a819a","order_by":5,"name":"Ting Song","email":"","orcid":"","institution":"School of Public Health, Kunming Medical University, Kunming, Yunnan","correspondingAuthor":false,"prefix":"","firstName":"Ting","middleName":"","lastName":"Song","suffix":""},{"id":371534267,"identity":"40fa4ac4-7aed-4d8e-a6c8-1b9aacc890aa","order_by":6,"name":"Jinshu Sun","email":"","orcid":"","institution":"Department of Gastroenterology, The People's Hospital of Lincang, Lincang, Yunnan","correspondingAuthor":false,"prefix":"","firstName":"Jinshu","middleName":"","lastName":"Sun","suffix":""},{"id":371534268,"identity":"8004729b-bb0d-4cc3-b4ce-4e4fd136a692","order_by":7,"name":"Ying Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIie3PsWrDMBCA4UsP5OXAq4Ihz3BGYDKE+FVsDO5i2kBewMGQLHmAQPsQeYUg4tVrhgyBQKYM6VIyGNoq0NXyWKj+QSC4D+kAXK6/mDQHA/iIu9ONJ6P+ZLgSWbiZ5aonMaqhKKCbTq3Cf6vqM82O01KDUhPGBDy933Y+cqyfFfElW1SQngsWL0B5fugiLIuf/7DOEEGrgmkOkiILef18EIGDZTBmmZZ2UghDpoQoAmC2E3nIo+E760SiwHDNiRK2XfxNdpHXVsdx03yc7u3XyPd03UlMTwSQlr83YRs3De4AcZ9Bl8vl+qd9A1wfREc0Fo4sAAAAAElFTkSuQmCC","orcid":"","institution":"School of Public Health, Kunming Medical University, Kunming, Yunnan","correspondingAuthor":true,"prefix":"","firstName":"Ying","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-10-27 07:53:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5340183/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5340183/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12876-025-03765-7","type":"published","date":"2025-03-21T15:57:41+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":68477426,"identity":"75d46352-943f-4d3d-a0da-2274685487b4","added_by":"auto","created_at":"2024-11-07 16:18:28","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":33358,"visible":true,"origin":"","legend":"\u003cp\u003eCross validation plot of Lasso regression model\u003c/p\u003e","description":"","filename":"Fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5340183/v1/f7fa8730bd900944e8886ae4.jpg"},{"id":68477427,"identity":"137f74f2-0a7e-4172-a581-aa2adea6c79e","added_by":"auto","created_at":"2024-11-07 16:18:28","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":36309,"visible":true,"origin":"","legend":"\u003cp\u003eBayesian network of gastric cancer constructed by hc algorithm\u003c/p\u003e","description":"","filename":"Fig.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5340183/v1/b753d537f2adabf3cf72741d.jpg"},{"id":68477428,"identity":"3d16b055-915a-4bac-a917-9f679f3b8e3e","added_by":"auto","created_at":"2024-11-07 16:18:28","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":60572,"visible":true,"origin":"","legend":"\u003cp\u003eBayesian network risk inference for gastric cancer\u003c/p\u003e","description":"","filename":"Fig.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5340183/v1/ec681df7e49a9d3135b790be.jpg"},{"id":79120657,"identity":"6693ba32-47b2-43e8-9e06-ee7f2ddceccb","added_by":"auto","created_at":"2025-03-24 16:10:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":997675,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5340183/v1/067e02e6-1b6b-4f69-84e9-72650aaff92f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Factors influencing the incidence of early gastric cancer: A Bayesian Network analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGastric cancer (GC) is a malignant tumor characterized by the abnormal proliferation of epithelial cells in the gastric mucosa, making it one of the most prevalent malignancies worldwide and posing a significant threat to human health\u003csup\u003e[1, 2]\u003c/sup\u003e. According to the latest report from the International Agency for Research on Cancer (IARC), there were approximately 19.96\u0026nbsp;million new cancer cases and 9.74\u0026nbsp;million cancer-related deaths globally in 2022. Among these, the number of new gastric cancer cases was 966,000, with nearly 660,000 deaths, ranking fifth in both incidence and mortality worldwide\u003csup\u003e[2]\u003c/sup\u003e. In China, there were 358,700 new gastric cancer cases and 260,400 deaths, positioning its incidence as the fifth highest among all cancers and its mortality as the third highest\u003csup\u003e[3]\u003c/sup\u003e. Although the incidence and mortality rates of gastric cancer in China have exhibited a downward trend from 1990 to 2019, in 2022, the country accounted for 37.0% of the world's new gastric cancer cases and 39.4% of gastric cancer deaths, indicating that the overall burden of incidence and mortality remains substantial\u003csup\u003e[4, 5]\u003c/sup\u003e. Gastric cancer has emerged as a major public health challenge in China.\u003c/p\u003e \u003cp\u003eThe five-year survival rate for patients with early-stage gastric cancer can reach as high as 85%\u003csup\u003e[6]\u003c/sup\u003e. However, due to the subtle nature of early symptoms, most patients are diagnosed at more advanced stages\u003csup\u003e[7]\u003c/sup\u003e. Even with prompt treatment at this stage, the five-year survival rate declines to below 10%\u003csup\u003e[6]\u003c/sup\u003e. Consequently, the implementation of gastric cancer screening is essential for improving prognosis and enhancing patient survival rates\u003csup\u003e[8]\u003c/sup\u003e. Endoscopic examination combined with pathological tissue biopsy is regarded as the \"gold standard\" for early gastric cancer screening\u003csup\u003e[9\u0026ndash;11]\u003c/sup\u003e. However, imaging and endoscopic procedures often entail high costs and depend on advanced equipment and physician expertise, rendering them difficult to implement in rural areas and regions with limited medical resources\u003csup\u003e[12]\u003c/sup\u003e. Furthermore, the discomfort associated with endoscopic procedures leads to low acceptance among asymptomatic patients, impeding widespread application\u003csup\u003e[13]\u003c/sup\u003e. In China, gastric cancer screening primarily targets symptomatic patients and relies on opportunistic endoscopic screenings. The uneven socioeconomic development and healthcare resource allocation across various regions in China have posed significant challenges to the comprehensive implementation of endoscopic screening within the population\u003csup\u003e[14]\u003c/sup\u003e. Given the low compliance and accessibility of endoscopic screening, investigating the factors influencing the incidence of early gastric cancer and developing risk models can offer valuable support for early intervention strategies.\u003c/p\u003e \u003cp\u003eThe development of gastric cancer is influenced by various risk factors\u003csup\u003e[2, 8]\u003c/sup\u003e. Machine learning methods can intelligently and efficiently identify these risk factors within populations. The Least Absolute Shrinkage and Selection Operator (Lasso) regression\u003csup\u003e[15]\u003c/sup\u003e effectively addresses multicollinearity issues in high-dimensional data, thereby facilitating the identification of significant factors influencing gastric cancer incidence. Bayesian Networks (BN) serve as a valuable machine learning tool for risk prediction in medical science, allowing for more precise risk stratification compared to traditional models. They also display the network structure of variable relationships and enable the construction of personalized risk prediction models\u003csup\u003e[16, 17]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eLincang City, situated in the southwestern region of Yunnan Province, is characterized by its diverse population, which includes 23 ethnic minorities. In recent years, there has been a notable increase in both the incidence and mortality rates of malignant tumors in this city. According to tumor monitoring data from 2019, Lincang City reported 3,080 new cases of malignant tumors, resulting in an incidence rate of 181.71 per 100,000 individuals. Furthermore, there were 1,447 deaths attributed to malignant tumors, yielding a mortality rate of 85.37 per 100,000 individuals\u003csup\u003e[18]\u003c/sup\u003e. Importantly, the incidence rate of gastric cancer in Lincang City was found to be 1.62 times higher than the average incidence rate in Yunnan Province, while the mortality rate was 1.80 times higher than the provincial average\u003csup\u003e[19]\u003c/sup\u003e. Consequently, addressing the rising incidence and mortality rates of gastrointestinal malignant tumors, particularly gastric cancer, has emerged as a pressing public health challenge that requires immediate attention in Lincang City. This study employs Lasso regression and Bayesian network predictive models to assess and predict the risk of gastric cancer, aiming to provide more accurate support for clinical early diagnosis and prevention.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and settings\u003c/h2\u003e \u003cp\u003eWe conducted a cross-sectional study with 2022\u0026ndash;2023 in the People's Hospital of Lincang to screen for diseases and questionnaires among people at high risk of gastric cancer. The definition of people at high risk of gastric cancer was referred to the Gastric Cancer Screening and Early Diagnosis and Treatment Programme (2024 Edition) (hereinafter referred to as the Programme) issued by the National Health Commission of the PRC\u003csup\u003e[20]\u003c/sup\u003e. The screening was performed in accordance with the objectives and methodology provided for in the programme. Initially, we included all eligible high-risk groups while excluding those who were younger than 40 years of age, non-residents of Lincang city (\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;2 years of local residence), and serious mental or physical illnesses or unwillingness to cooperate. This study was approved by the Ethics Committee of Kunming Medical University (Approval No. KMMU2024MEC021) prior to commencement, and informed consent was obtained from all participants.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy samples\u003c/h3\u003e\n\u003cp\u003eA total of 1820 gastric cancer high-risk individuals were included in this study, and were divided into gastric cancer group (n\u0026thinsp;=\u0026thinsp;129) and non-gastric cancer group (n\u0026thinsp;=\u0026thinsp;1691) according to whether they developed gastric cancer or not. Gastroscopy results in the Guidelines for the Diagnosis and Treatment of Gastric Cancer (2022 Edition)\u003csup\u003e[21]\u003c/sup\u003e were utilized to determine whether the patient had developed gastric cancer.\u003c/p\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cp\u003eWe developed a questionnaire for the assessment of people at risk of upper gastrointestinal cancer in accordance with the Programme. The questionnaire included four main domains: general characteristics (gender, age, ethnicity and current address), upper gastrointestinal symptoms (bloating, heartburn, acid reflux, nausea, hiccup, belching, eating discomfort, upper abdominal pain, and vomiting), risk factors for upper gastrointestinal cancers (smoking, alcohol consumption, scalding hot food, overspeed eating, indoor air pollution, toothlessness, pernicious anemia), precancerous diseases or lesions (gastric severe intestinal epithelial hyperplasia, giant folds of gastric mucosa sign, 10 years of postoperative gastric remnants after surgery for benign disease, more than 6 years of postoperative residual stomach after gastric, low-grade intraepithelial neoplasia, family history of upper gastrointestinal tract cancer, stomach polyp, types of gastritis, gastric ulcer) and gastroscopy for gastric tumors.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll analyses were undertaken using R4.3.3 and its installation package and Netical software. Firstly, \u0026ldquo;glment\u0026rdquo; package was used for Lasso regression to screen for variables that have a significant impact on gastric cancer. Lasso\u003csup\u003e[15]\u003c/sup\u003e, based on the least squares method, allows for the shrinkage of less significant variable coefficients to zero while retaining the coefficients of important variables. Lasso regression effectively addresses multicollinearity issues among variables and identifies those of notable significance.\u003c/p\u003e \u003cp\u003eSecondly, to develop our model, we split data into a 70% training dataset using the \u0026ldquo;caret\u0026rdquo; package and a 30% test dataset. Subsequently, in the training dataset, we employed the variables screened by lasso regression to construct a Bayesian network model with \u0026ldquo;bnlearn\u0026rdquo; package, the hill-climbing algorithm (hc) for structural learning, and great likelihood estimation to learn the parameters. Bayesian Networks (BN) are models that simulate the uncertainty of causal relationships during reasoning processes\u003csup\u003e[22, 23]\u003c/sup\u003e. The model primarily consists of a directed acyclic graph and a collection of conditional probability tables. In this framework, nodes represent variables, with each node corresponding to a specific variable. Directed arcs connecting two nodes indicate direct probabilistic dependencies, while conditional probabilities express the strength of relationships among the nodes. Sub-nodes are those to which directed arcs point, while their parent nodes are those from which the arrows originate. The direction of the arrows qualitatively describes the relationships among the nodes.\u003c/p\u003e \u003cp\u003eAfter the model was constructed, accuracy, area under the curve (AUC) and sensitivity and specificity were used to evaluate model discrimination. Finally, Netica software was applied to draw the topology of the Bayesian network and carry out Bayesian inference.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDemographic characteristics\u003c/h2\u003e \u003cp\u003eA total of 1,820 eligible patients were included in the study, comprising 743 males (40.82%) and 1,077 females (59.18%), with a mean age of 56.22\u0026thinsp;\u0026plusmn;\u0026thinsp;7.64 years. Among the participants, 129 cases of gastric cancer (7.09%) were identified, while 1,691 patients (92.91%) were diagnosed with non-gastric cancer.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneral characteristics of patients [n (%)]\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGC patients (N\u0026thinsp;=\u0026thinsp;129)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-GC patients (N\u0026thinsp;=\u0026thinsp;1691)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOverall (N\u0026thinsp;=\u0026thinsp;1820)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57 (44.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e686 (40.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e743 (40.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 (55.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1005 (59.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1077 (59.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56 (43.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e665 (39.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e721 (39.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73 (56.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1026 (60.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1099 (60.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHan ethnic group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 (57.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e762 (45.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e836 (45.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWa ethnic group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (21.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e310 (18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e338 (18.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDai ethnic group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e235 (13.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e242 (13.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYi ethnic group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e148 (8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e161 (8.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther ethnic groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e236 (14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e243 (13.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent address\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhenkang, Gengma and Cangyuan county\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (38.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e704 (41.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e754 (41.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShuangjiang, Linxiang and Yunxian county\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (37.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e737 (43.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e785 (43.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFengqing and Yongde county\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (24.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e250 (14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e281 (15.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBloating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69 (53.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e951 (56.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1020 (56.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60 (46.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e740 (43.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e800 (44.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeartburn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103 (79.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1453 (85.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1556 (85.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e238 (14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e264 (14.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcid reflux\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93 (72.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1365 (80.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1458 (80.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (27.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e326 (19.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e362 (19.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNausea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103 (79.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1491 (88.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1594 (87.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e200 (11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e226 (12.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHiccup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119 (92.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1579 (93.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1698 (93.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e112 (6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e122 (6.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBelching\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e112 (86.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1551 (91.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1663 (91.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e140 (8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e157 (8.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEating discomfort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104 (80.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1462 (86.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1566 (86.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e229 (13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e254 (14.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpper abdominal pain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53 (41.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e596 (35.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e649 (35.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76 (58.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1095 (64.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1171 (64.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVomiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119 (92.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1636 (96.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1755 (96.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65 (3.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103 (79.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1336 (79.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1439 (79.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e355 (21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e381 (20.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e124 (96.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1531 (90.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1655 (90.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e160 (9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e165 (9.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScalding hot food\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109 (84.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1427 (84.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1536 (84.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (15.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e264 (15.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e284 (15.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverspeed eating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100 (77.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1268 (75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1368 (75.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e423 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e452 (24.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndoor air pollution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1687 (99.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1816 (99.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4 (0.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eToothlessness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65 (50.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e851 (50.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e916 (50.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64 (49.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e840 (49.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e904 (49.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePernicious anemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127 (98.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1683 (99.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1810 (99.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10 (0.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastric severe intestinal epithelial hyperplasia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127 (98.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1688 (99.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1815 (99.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5 (0.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGiant folds of gastric mucosa sign\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e128 (99.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1690 (99.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1818 (99.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2 (0.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10 years of postoperative gastric remnants after surgery for benign disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e128 (99.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1690 (99.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1818 (99.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2 (0.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than 6 years of postoperative residual stomach after gastric cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127 (98.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1686 (99.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1813 (99.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7 (0.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-grade intraepithelial neoplasia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1689 (99.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1818 (99.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2 (0.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history of upper gastrointestinal tract cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121 (93.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1677 (99.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1798 (98.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22 (1.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardia polyp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e128 (99.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1669 (98.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1797 (98.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23 (1.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStomach polyp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91 (70.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1214 (71.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1305 (71.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (29.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e477 (28.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e515 (28.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTypes of gastritis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrophic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e230 (13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e243 (13.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-atrophic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99 (76.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1128 (66.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1227 (67.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDissipated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36 (2.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHybrid type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e281 (16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e288 (15.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26 (1.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastric ulcer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e123 (95.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1627 (96.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1750 (96.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70 (3.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eVariable Selection Using Lasso Regression\u003c/h3\u003e\n\u003cp\u003eA total of 30 influencing factors were included in the Lasso regression analysis. Through five-fold cross-validation, seven clinically significant indicators were identified: ethnicity, upper gastrointestinal symptoms (nausea, acid reflux, vomiting), alcohol consumption, severe gastric intestinal metaplasia, and family history of upper gastrointestinal cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eConstruction of the Bayesian Network Model\u003c/h3\u003e\n\u003cp\u003eUsing the seven variables selected from the Lasso regression model, a Bayesian network predictive model was constructed to explore the relationships between gastric cancer and its associated influencing factors, employing the hc algorithm. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the resulting network model comprised eight nodes and twelve edges. The analysis revealed that alcohol consumption, family history of upper gastrointestinal cancer, severe gastric intestinal metaplasia, vomiting, nausea, and acid reflux are directly associated with the occurrence of gastric cancer, while ethnicity is indirectly related through alcohol consumption.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eInference of the Bayesian Network Model\u003c/h2\u003e \u003cp\u003eThe constructed network model was employed for predictive assessments, utilizing relevant node information to evaluate the risk of gastric cancer in patients. For instance, when a patient has a history of alcohol consumption but exhibits no other disease symptoms, regardless of ethnicity, Bayesian network inference indicates that the likelihood of developing gastric cancer decreases to 7.35% (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Conversely, when a patient is diagnosed with severe gastric intestinal metaplasia and presents with symptoms of nausea and acid reflux, the risk of developing gastric cancer escalates to 55.2% (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of the Bayesian Network Model\u003c/h2\u003e \u003cp\u003eThe results showed that 505 patients were correctly classified, yielding a classification accuracy of 92.66%, a sensitivity of 15.79%, and a specificity of 98.22%. The area under the curve (AUC) for the model evaluated on the test set was 0.615, indicating that the constructed Bayesian network model performed effectively and accurately represented the relationships among the various nodes.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWith the aging population and the rising prevalence of unhealthy lifestyles, the burden of gastric cancer in China is gradually increasing\u003csup\u003e[2]\u003c/sup\u003e. The occurrence and progression of gastric cancer are influenced by multiple factors; identifying these direct and indirect influencing factors can facilitate the early identification of high-risk populations, improve the levels of early diagnosis and treatment\u003csup\u003e[1]\u003c/sup\u003e, and mitigate the loss of quality of life associated with gastric cancer. Consequently, employing a Bayesian network to construct a diagram of the influencing factors elucidates the complex relationships among them, thereby contributing positively to the reduction of gastric cancer incidence.\u003c/p\u003e \u003cp\u003eThe findings of this study indicate that alcohol consumption, ethnicity, family history of upper gastrointestinal cancer, vomiting, acid reflux, nausea, and severe gastric intestinal metaplasia are significant influencing factors for the occurrence of gastric cancer. Ethnicity is directly related to gastric cancer incidence. Lincang, a city located on the southwestern border of China, there is a notable concentration of ethnic minorities. The Wa, Dai, and Yi ethnic groups were the main ethnic minorities in the region. Unique customs and dietary practices, such as the consumption of raw meat and untreated water, may contribute to the elevated incidence of gastric cancer\u003csup\u003e[24]\u003c/sup\u003e, suggesting that ethnic areas are a part of the prevention and treatment of gastric cancer.\u003c/p\u003e \u003cp\u003ePrevious studies have reported that the risk of gastric cancer were higher among alcohol consumers compared to non-drinkers\u003csup\u003e[8, 25]\u003c/sup\u003e. However, our study revealed that alcohol consumption may actually reduce the risk of gastric cancer. A meta-analysis of cohort studies examining the relationship between alcohol consumption and gastric cancer risk indicated that light to moderate drinking does not significantly affect the risk of gastric cancer compared to non-drinkers\u003csup\u003e[26]\u003c/sup\u003e. This phenomenon may be attributed to the antibacterial effects of alcohol against Helicobacter pylori\u003csup\u003e[27, 28]\u003c/sup\u003e. Furthermore, some research has suggested that male light to moderate drinkers (1-5g/day) exhibit the lowest risk of alcohol-related cancer mortality\u003csup\u003e[29]\u003c/sup\u003e, implying that moderate alcohol consumption may have a protective effect against gastric cancer. Future studies should aim to further investigate the dose-response relationship between alcohol consumption and the occurrence of gastric cancer, as well as explore the underlying mechanisms involved. Moreover, alcohol has been identified as a risk factor for various cancers, including esophageal and liver cancer\u003csup\u003e[30]\u003c/sup\u003e, highlighting the necessity for further validation of this conclusion.\u003c/p\u003e \u003cp\u003eSevere gastric intestinal metaplasia was recognized as a precancerous lesion for gastric cancer\u003csup\u003e[31]\u003c/sup\u003e. Over time, chronic atrophic gastritis can lead to the replacement of gastric mucosal epithelial cells with intestinal epithelial cells, facilitating the progression of precancerous lesions to the intestinal metaplasia stage. Early diagnosis of intestinal metaplasia, followed by appropriate medical intervention and endoscopic treatment, can alleviate clinical symptoms, enhance the physiological function of the gastric mucosa, and reduce or delay the onset of gastric cancer\u003csup\u003e[32]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAcid reflux, vomiting, and nausea were common upper gastrointestinal symptoms associated with various gastrointestinal diseases and served as important predictive indicators for gastric cancer risk. These clinical manifestations may suggest a history of gastrointestinal disorders, such as intestinal metaplasia. A meta-analysis has indicated that the risk of gastric cancer among patients with gastrointestinal diseases and chronic gastrointestinal conditions is 4.85 times and 4.40 times greater, respectively, than that of the general population\u003csup\u003e[33]\u003c/sup\u003e. Therefore, when patients present with frequent upper gastrointestinal symptoms, further investigation, including gastroscopy and pathological biopsy, when necessary, should be conducted. Furthermore, in alignment with the findings of Zhang Linglin\u003csup\u003e[34]\u003c/sup\u003e and EOM\u003csup\u003e[35]\u003c/sup\u003e, a family history of upper gastrointestinal cancer is a well-established risk factor for gastric cancer. Studies indicate that 10%-15% of gastric cancer patients have a familial history of gastric tumors\u003csup\u003e[36]\u003c/sup\u003e, with a significantly elevated incidence observed among first-degree relatives of gastric cancer patients\u003csup\u003e[37]\u003c/sup\u003e. This increased risk may be attributed to shared genetic loci within families\u003csup\u003e[38]\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThe limitations of this study include the fact that all samples were sourced from a single institution, which restricts the generalizability of the findings to the broader population. Consequently, future research should involve multiple institutions across diverse geographic regions to increase the sample size and obtain more relevant indicators for a comprehensive investigation. This approach would enhance the practical applicability and robustness of the model.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study employed Lasso regression for variable selection, effectively addressing the issue of multicollinearity among variables and thereby identifying those of significant importance. Subsequently, a Bayesian network was utilized for structural and parameter learning, allowing for the calculation of the impact of various factors on the early risk of gastric cancer. This approach visually illustrates the direct and indirect relationships among the influencing factors, clarifying the internal regulatory mechanisms that govern these relationships. By overcoming the limitations of traditional predictive models in explaining causal relationships and probability calculations, this methodology aids healthcare professionals in the early identification of high-risk populations for gastric cancer. Furthermore, it enhances the levels of early diagnosis and treatment, facilitating timely targeted interventions to mitigate the risk of gastric cancer and reduce the associated loss of quality of life.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eGC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;gastric cancer\u003c/p\u003e\n\u003cp\u003eLasso \u0026nbsp; \u0026nbsp;Least Absolute Shrinkage and Selection Operator\u003c/p\u003e\n\u003cp\u003eBN \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Bayesian Networks\u003c/p\u003e\n\u003cp\u003eAUC \u0026nbsp; \u0026nbsp; area under the curve\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge the collaborative efforts of the study participants and data collectors in the designated survey area.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRYL, TMY and YC conceived the study and designed the protocol. ZD, YG and NL supervised the implementation of the study and advised on data analysis. RYL, TMY, TS and JSS were involved in the development of the questionnaires, data collection and analysis. RYL drafted the original manuscript. YC revised the original manuscript. All authors reviewed and approved the final manuscript. YC and RD are the sponsors of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRole of funding source This study was supported by a grant from Yunnan Provincial Talent Program for Young Scholar and Technical Reserve Personnel (202305AC160046), National Key Scientific and Technological Project for Sustainable Development Demonstration Zones (202104AC100001-A11)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed in this study are available from the corresponding author on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study protocol was approved by the Ethics Committee of Kunming Medical University (Approval No. KMMU2024MEC021) prior to commencement.\u0026nbsp;Adhering to the principle of voluntary participation, all potential participants were given the opportunity to make an informed decision on their participation in the study.\u0026nbsp;All subjects were clearly informed of their right to withdraw from the study at any time without facing adverse consequences.\u0026nbsp;To ensure transparency, the purpose and procedures of the study were fully explained to the subjects before signing the informed consents.\u0026nbsp;Individually identifiable information, such as name and telephone number, was deliberately omitted from the recorded data during the data collection phase to ensure anonymity.\u0026nbsp;Finally, Information collected was subjected to appropriate coding procedures and kept strictly confidential throughout the research process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMACHLOWSKA J, BAJ J, SITARZ M, et al. 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J Gastroenterol Hepatol, 2000, 15(3): 271-6.https://doi.org/10.1046/j.1440-1746.2000.02077.x\u003c/li\u003e\n\u003cli\u003eXIAOLONG C. A Study on The Association Between Drinking Patterns and Gastric Cancer and Precancerous Lesions in Wuwei Cohort [D]; Lanzhou University, 2023.\u003c/li\u003e\n\u003cli\u003eCAO Y, WILLETT W C, RIMM E B, et al. Light to moderate intake of alcohol, drinking patterns, and risk of cancer: results from two prospective US cohort studies [J]. BMJ, 2015, 351: h4238.https://doi.org/10.1136/bmj.h4238\u003c/li\u003e\n\u003cli\u003eRUMGAY H, SHIELD K, CHARVAT H, et al. Global burden of cancer in 2020 attributable to alcohol consumption: a population-based study [J]. Lancet Oncol, 2021, 22(8): 1071-80.https://doi.org/10.1016/S1470-2045(21)00279-5\u003c/li\u003e\n\u003cli\u003eYAOFU F, YANMIN W, HAO L, et al. Analysis of risk factors associated with precancerous lesion of gastric cancer in patients from eastern China: a comparative study [J]. 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PLoS One, 2015, 10(7): e0132613.https://doi.org/10.1371/journal.pone.0132613\u003c/li\u003e\n\u003cli\u003eZENGYUN W, JING N, YUE W, et al. Research progress on risk factors of gastric cancer in families [J]. China Medicine, 2023, 18(08): 1264-7.\u003c/li\u003e\n\u003cli\u003eSHIN C M, KIM N, YANG H J, et al. Stomach cancer risk in gastric cancer relatives: interaction between Helicobacter pylori infection and family history of gastric cancer for the risk of stomach cancer [J]. J Clin Gastroenterol, 2010, 44(2): e34-9.https://doi.org/10.1097/MCG.0b013e3181a159c4\u003c/li\u003e\n\u003cli\u003eCHEN B, WANG Y, TANG W, et al. Association between PPARgamma, PPARGC1A, and PPARGC1B genetic variants and susceptibility of gastric cancer in an Eastern Chinese population [J]. BMC Med Genomics, 2022, 15(1): 274.https://doi.org/10.1186/s12920-022-01428-0\u003c/li\u003e\n\u003c/ol\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-gastroenterology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmge","sideBox":"Learn more about [BMC Gastroenterology](http://bmcgastroenterol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmge/default.aspx","title":"BMC Gastroenterology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Gastric cancer, Bayesian networks, Machine learning, Lasso regression","lastPublishedDoi":"10.21203/rs.3.rs-5340183/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5340183/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThis study aims to establish a Bayesian network risk prediction model for gastric cancer using data mining methods. It explores both direct and indirect factors influencing the incidence of gastric cancer and reveals the interrelationships among these factors.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData were collected from early cancer screenings conducted at the People's Hospital of Lincang between 2022 and 2023. A Lasso regression model was utilized for preliminary variable selection, and the Bayesian network model was constructed using R software. The network structure analysis was visualized with Netica software, followed by inference and evaluation of the model.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe incidence rate of gastric cancer in this region high-risk population was determined to be 7.09%. The Lasso regression model identified several risk factors for gastric cancer, including ethnicity, upper gastrointestinal symptoms (nausea, acid reflux, vomiting), alcohol consumption, severe gastric intestinal metaplasia, and a family history of upper gastrointestinal cancers. A total of seven risk factors were incorporated into the Bayesian network model, resulting in a network structure consisting of eight nodes and twelve edges. The area under the curve (AUC) for the network model was found to be 0.615.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe Bayesian network model provides an intuitive framework for understanding the direct and indirect factors contributing to the early onset of gastric cancer, elucidating the interrelationships among these factors. Furthermore, the model demonstrates satisfactory predictive performance, which may facilitate the early detection of gastric cancer and enhance the levels of early diagnosis and treatment among high-risk populations.\u003c/p\u003e","manuscriptTitle":"Factors influencing the incidence of early gastric cancer: A Bayesian Network analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-07 16:18:24","doi":"10.21203/rs.3.rs-5340183/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-01-06T17:53:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-24T14:58:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-14T20:48:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"211395780600731944773511729398872300329","date":"2024-12-13T17:04:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"31781473745780371539797482074045751815","date":"2024-12-12T18:50:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-18T11:38:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-14T14:55:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-29T04:14:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Gastroenterology","date":"2024-10-27T07:37:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-gastroenterology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmge","sideBox":"Learn more about [BMC Gastroenterology](http://bmcgastroenterol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmge/default.aspx","title":"BMC Gastroenterology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"10912dec-f03f-4976-b063-bd6da5b620d9","owner":[],"postedDate":"November 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-24T16:06:05+00:00","versionOfRecord":{"articleIdentity":"rs-5340183","link":"https://doi.org/10.1186/s12876-025-03765-7","journal":{"identity":"bmc-gastroenterology","isVorOnly":false,"title":"BMC Gastroenterology"},"publishedOn":"2025-03-21 15:57:41","publishedOnDateReadable":"March 21st, 2025"},"versionCreatedAt":"2024-11-07 16:18:24","video":"","vorDoi":"10.1186/s12876-025-03765-7","vorDoiUrl":"https://doi.org/10.1186/s12876-025-03765-7","workflowStages":[]},"version":"v1","identity":"rs-5340183","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5340183","identity":"rs-5340183","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}