Latent Classification and Psychological Associations with Quality of Life in Chronic Gastrointestinal Disease Patients: A Cross-Sectional Study

Latent Classification and Psychological Associations with Quality of Life in Chronic Gastrointestinal Disease Patients: A Cross-Sectional Study | 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 Latent Classification and Psychological Associations with Quality of Life in Chronic Gastrointestinal Disease Patients: A Cross-Sectional Study Wang Rui, Caiyi Yang, Baocheng Sun, Wenhui Zhang, Shimin Chen, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8357816/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Objective To explore the latent classification of patients with chronic gastrointestinal diseases using the Patient reported outcomes instrument for chronic gastrointestinal diseases (PRO) scale, and analyze the correlations between psychological status and quality of life. Methods PRO scale, SAS/SDS scores were collected from chronic gastrointestinal disease patients. Latent Class Analysis (LCA) was used to determine the optimal number of latent classes. Structural Equation Modeling (SEM) assessed correlations between symptom groups, and Artificial Neural Networks (ANN) analyzed the relationship between anxiety, depression, and symptom clusters. Results A total of 290 valid questionnaires were collected. Based on LCA, PRO scores were divided into three latent classes: 33.79%, 37.93%, and 28.28%. Univariate analysis showed that education level significantly affected classification (p = 0.029). SEM analysis showed that all six symptom clusters significantly impacted latent classes, with dyspepsia (regression coefficient = 0.71) having the most significant effect; Strong positive correlations were observed between general symptoms and indigestion (r = 0.647) and reflux (r = 0.538). ANN showed that anxiety and depression influenced symptom clusters, especially dyspepsia. Conclusion This study reveals the complex interplay between symptoms and psychological factors in chronic gastrointestinal diseases. The identification of distinct symptom groups emphasizes the need for personalized treatment strategies. Addressing psychological factors like anxiety and depression, alongside physical symptoms, can enhance the management and quality of life for patients. Chronic gastrointestinal diseases Latent Class Analysis Structural Equation Modeling Artificial Neural Networks Psychological status Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction With the in-depth study of the relationship between mental health and physical diseases, the important role of psychological factors in chronic diseases has gained increasing attention. Common psychological states, such as anxiety and depression, are not only core issues in the field of mental health but also play a key role in the onset and progression of many physical diseases (Bisgaard et al., 2022). Particularly in chronic gastrointestinal diseases, psychological factors influence patients' clinical symptoms, disease progression, and quality of life through a complex neuro-endocrine-immune regulatory network, gradually becoming a hot topic in interdisciplinary research (Warren et al., 2024). Patient reported outcomes instrument for chronic gastrointestinal diseases (PRO-GI) scale, as an important tool reflecting the patient's subjective health status and quality of life, has been widely used in the research and management of chronic gastrointestinal diseases (Tang et al., 2018). The selection of dimensions such as dyspepsia, reflux, and abnormal bowel movements was driven by the need to reflect the complexity of symptom clusters rather than isolated symptoms (e.g., abdominal pain). The PRO-GI scale encompasses six dimensions: general symptoms, dyspepsia, reflux, abnormal bowel movements, psychological factors, and social factors, thereby capturing interactions between symptom groups and psychological traits. This multidimensional approach allows for the prediction of disease progression and provides a foundation for personalized interventions. The scale contains 39 questions, including 6 on general symptoms, 10 on dyspepsia, 8 on reflux symptoms, 9 on abnormal bowel movements, and 3 each on psychological and social functioning issues. By assessing these dimensions, the PRO scale can reveal patients' symptom heterogeneity and the association with mental health, providing a basis for personalized treatment plans. However, most existing studies are limited to single statistical analysis methods, making it difficult to comprehensively reveal the complex relationship between PRO, psychological characteristics, and demographic factors. Multivariate statistical methods, such as Latent Class Analysis (LCA), can provide detailed classification of patient populations, but have limitations in exploring the complex relationships between variables (Sinha et al. 2021). Structural Equation Modeling (SEM), as a multidimensional correlation analysis tool, can intuitively display the path relationships between latent variables and their impact mechanisms(Li & Jacobucci, 2022; Fu et al., 2022). Machine learning methods, such as Artificial Neural Networks (ANN), have significant advantages in handling nonlinear data relationships (Hong et al. 2024). To our knowledge, this is the first study to integrate LCA, SEM, and ANN, overcoming the limitations of traditional single-method approaches. This multimodal framework provides a multidimensional perspective on symptom-psychological interactions, enabling both classification and mechanistic exploration of heterogeneous patient groups. Combining these methods offers an innovative strategy for analyzing the latent classification characteristics of the PRO scale and its association with psychological traits. This study aims to utilize the advantages of statistical methods and machine learning by combining LCA for latent classification of patient groups, analyzing path relationships between latent variables through SEM, and introducing ANN to further explore the complex interaction mechanisms between the PRO scale, psychological characteristics, and demographic factors, with the expectation of providing more precise theoretical guidance for clinical interventions. Study Subjects and Methods To provide a more comprehensive understanding of symptom clustering and its underlying mechanisms, we employed a combination of LCA, SEM, and ANN. While LCA identified distinct patient subgroups based on symptom presentation, SEM elucidated the relationships among symptom clusters, and ANN quantified the contribution of psychological factors to latent classifications. 1.1. Study Subjects The study selected chronic gastrointestinal disease patients admitted to the First Affiliated Hospital of Anhui University of Chinese Medicine from March 2022 to March 2023 as the research subjects. Inclusion Criteria: ①Clinically diagnosed with chronic gastritis, including chronic non-atrophic gastritis, chronic atrophic gastritis, and special types of gastritis (Chinese Society of Gastroenterology et al. 2023), peptic ulcer (Kamada et al. 2021), gastroesophageal reflux disease (Iwakiri et al. 2022), functional dyspepsia (Miwa et al. 2022), chronic diarrhea (Ihara et al. 2024), ulcerative colitis (Rubin et al. 2019), gastric polyps and intestinal polyps (Tanaka et al. 2021). The inclusion of both functional and organic gastrointestinal diseases aimed to reflect real-world clinical diversity. The diagnoses of the above diseases are based on guidelines or expert consensus. ②Aged 18 years or older. ③Literate at the elementary school level or above, capable of reading and understanding the questionnaire. ④Disease duration of at least 1 month. Exclusion Criteria: ①Presence of other organic diseases requiring treatment. ②Inconsistent or superficial responses, where the scale results do not align with the patient's usual performance. This study used SEM to analyze the 6 dimensions of the Chronic Gastrointestinal Disease Patient-Reported Outcomes PRO scale. According to the rule of thumb, each model parameter that needs to be estimated requires at least 20 samples, thus a total of 120 samples were needed. ANN were employed to investigate the latent effects of the Self-Rating Anxiety Scale (SAS) and Self-Rating Depression Scale (SDS) on PRO. Based on the Baum-Haussler formula, 284 samples were needed for a 95% confidence interval. The study was approved by the Ethics Committee of the First Affiliated Hospital of Anhui University of Chinese Medicine (Approval No. 2022MCZQ04). 1.2 Research Content 1.2.1 General Information This includes demographic characteristics and disease-related information. The demographic characteristics include gender, age, education level, marital status, employment status, monthly income, etc. The disease-related information includes the disease diagnosis, duration, medication history, etc. 1.2.2 Patient-Reported Outcome Measures for Chronic Gastrointestinal Diseases The patient reported outcomes instrument for chronic gastrointestinal diseases (PRO-GI) is a scale specifically used to assess chronic gastrointestinal disease patients' symptoms, functional status, health-related quality of life (HRQoL), and other aspects of their subjective experience and clinical outcomes. Developed to capture the complexity of symptom clusters in chronic gastrointestinal diseases (Tang et al., 2018), the PRO-GI scale comprises 39 items classified into six dimensions: systemic symptoms, dyspepsia, reflux, abnormal bowel movements, psychological factors, and social factors. This multidimensional structure was validated in prior studies, demonstrating high reliability (Cronbach’s α = 0.85-0.93). Because gastrointestinal disease symptoms often overlap, the PRO-GI provides a holistic assessment of treatment outcomes across symptomatology, satisfaction, and quality of life. For each group of symptoms, responses are scored based on frequency: 0 points for "none," 1 point for "occasionally," 2 points for "sometimes," 3 points for "often," and 4 points for "always." Scores are calculated for each symptom group and a total score. The Cronbach’s α coefficient for this scale is 0.85, and in this study, the Cronbach’s α coefficient is 0.93. 1.2.3 Self-Rating Anxiety Scale and Self-Rating Depression Scale The Self-Rating Anxiety Scale (SAS) and Self-Rating Depression Scale (SDS) are self-report scales widely used in psychological health assessment (Zhang et al. 2024), developed by Zung. The SAS contains 20 items and is mainly used to assess the severity of anxiety symptoms, covering physical, emotional, and cognitive aspects. The score is calculated by multiplying the total score by 1.25 to obtain the standardized score. A score ≥50 indicates the presence of anxiety. The Cronbach's α coefficient of this scale is 0.91 (Tiksnadi et al. 2023), and in this study, the Cronbach's α coefficient is 0.82. Similarly, the SDS also includes 20 items and mainly assesses depression symptoms, covering both psychological and physical aspects. The scoring method is the same as that of the SAS, and a standardized score ≥53 suggests the possible presence of depression. The Cronbach's α coefficient of this scale is 0.87 (Tiksnadi et al. 2023), and in this study, the Cronbach's α coefficient is 0.85. In this study, SAS and SDS were used to assess patients' anxiety and depression status, providing a reference for the analysis of disease-related psychological factors. 1.3 Data Collection and Quality Control Methods All patient questionnaire data were collected in the hospital, with the researcher distributing the questionnaires face-to-face. Before the survey, the purpose and significance of the study were explained to the patients, and their consent was obtained before distributing the questionnaires. Patients were instructed to fill out the questionnaires independently. During the completion process, unified instructions were provided to explain how to fill out the questionnaires and answer any questions from patients. Disease-related information was collected by the researcher through electronic medical records or by inquiring with the patients. Once the questionnaires were completed, they were collected on-site, and the researcher checked the completion status. If any information was missing, patients were asked to complete it immediately. If there were patterns in the responses, or if all options were identical or the patient refused to answer additional questions, the questionnaire was deemed invalid. 1.4 Statistical Methods All data were entered by two researchers and cross-checked to ensure accuracy. Analytical Tools: Latent Class Analysis (LCA) was performed using the poLCA package in R (version 4.3.1) to identify the optimal latent classification of symptom clusters. Descriptive statistics (e.g., means, standard deviations) and hypothesis testing (e.g., ANOVA, χ² tests) were conducted using SPSS 26.0 (IBM Corp., Armonk, NY, USA). 1.4.1 Latent Class Analysis The analysis began with a single-class model, gradually increasing the number of classes until the model's fit indices reached optimal values. Fit indices include: ① Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and sample size-adjusted BIC (aBIC), with lower values indicating better model fit; ② Entropy, ranging from 0 to 1, with values closer to 1 indicating more accurate classification; ③ Bootstrap likelihood ratio test (BLRT), where a p-value < 0.05 indicates that the k-class model fits better than the k-1 class model (Dahmer et al. 2022). Finally, discriminant analysis was performed to verify the accuracy of the optimal model obtained from the latent profile analysis. 1.4.2 Comprehensive Data Analysis Methods Based on Latent Class Analysis (LCA), Structural Equation Modeling (SEM), and Artificial Neural Networks (ANN), the data were analyzed to explore the interrelationships and potential influencing factors of different symptom clusters in chronic gastrointestinal disease patients. First, SEM was applied to assess the latent structural relationships of the scale. The six symptom clusters from the PRO scale were used as latent variables, and the factor loadings and path coefficients of the observed indicators were examined to validate the associations and causal relationships between different symptom clusters in chronic gastrointestinal diseases. Next, an ANN model was applied to further analyze the data from the PRO, SAS, and SDS scales to predict the impact of external states, such as anxiety and depression, on the severity of symptoms in chronic gastrointestinal diseases. The six symptom clusters of the PRO scale were used as continuous predictors, with the total scores of the SAS and SDS as factor variables, and the latent classification of the PRO scale was set as the outcome. Model performance was evaluated using accuracy, precision, recall, and F1-score to predict the latent classification based on symptom clusters and anxiety/depression scores. The results from this comprehensive analysis highlight the complex relationship between psychological factors and gastrointestinal symptoms, offering deeper insights for targeted interventions in chronic gastrointestinal disease management. 1.4.3 Statistical Processing SPSS 26.0 statistical software was used for data analysis. Normally distributed continuous data were expressed as means ± standard deviations, and comparisons among multiple groups were performed using one-way analysis of variance (ANOVA). Count data were presented as frequencies and percentages, with comparisons between multiple groups using the χ² test or Fisher’s exact test. Multivariate logistic regression was used to further explore the factors influencing IBD patients' fear of disease progression. A p-value < 0.05 was considered statistically significant for all tests. 2. Results 2.1 General Information A total of 304 patients were included in this study, with 14 invalid questionnaires excluded. After data cleaning, 290 valid questionnaires were collected, resulting in a response rate of 95.39%. The PRO scores for each symptom cluster and total score are shown in Table 1. The distribution of general information for symptom clusters is presented in Table 3. 2.2 Common Method Bias Test Results The results of the common method bias test show that among the 39 questions of the PRO scale, there are 10 factors with eigenvalues > 1. The first principal component explained 29.85% of the variance (less than 40%), indicating that there is no significant common method bias. 2.3 Latent Classification Results of PRO Scale in Chronic Gastrointestinal Disease Patients Based on the average scores of the 6 symptom clusters of the Chronic Gastrointestinal Disease Patient Reported Outcomes (PRO), latent class models were fit. Starting from the initial model, the number of categories was gradually increased, establishing latent class models for 1 to 5 categories (Table 2). With an increase in the number of categories, AIC, BIC, and aBIC showed an overall decreasing trend. The BIC and aBIC were minimized in the 3-class model. The entropy value was around 0.1, indicating that the data structure was well-defined. The results suggest that the 3-class model (low/medium/high) is optimal and reliable. Based on the 3-class model, the mean scores for the PRO total score and each symptom cluster are presented in Figure 1. The three categories were named according to the fluctuation in mean item scores. Category 1 (C1) had a PRO total score of (21.26 ± 8.11), with a mean item score of 0.6, corresponding to the options "None" and "Occasionally." This group represents patients who can adapt to the impact of the disease on health and social aspects, thus named the "Low Risk-Disease Adaptation Group" (33.79% of patients). Category 2 (C2) had a PRO total score of (44.15 ± 11.11), with a mean item score of 1.3, corresponding to the options "Sometimes" and "Frequently," and was named the "Medium Risk-Disease Distress Group" (37.93%). Category 3 (C3) had a PRO total score of (70.79 ± 18.63), with a mean item score of 2.1, corresponding to the option "Always," and was named the "High Risk-Functional Impairment Group" (28.28%). The distribution of scores is shown in Figure 1. The univariate analysis based on latent classification showed no significant differences among the three latent categories in gender, age, residence, marital status, monthly income, occupation, and treatment history (P > 0.05). However, significant differences were observed in disease duration and education level. Longer disease duration was associated with higher symptom scores, indicating a positive correlation between disease duration and symptom severity. Regarding education, high school graduates were more likely to be in category 3 (35.07%), while those with lower than high school education showed an even distribution across categories. In contrast, individuals with a bachelor's degree or higher were predominantly in category 2 (45.45%). Detailed results are in Table 3. 2.4 Causal Structure Between PRO Scale Symptom Clusters in Chronic Gastrointestinal Diseases: SEM Analysis and Regression Coefficients The Pearson correlation analysis showed that the six symptom clusters had correlations ranging from 0.39 to 1.00, with means between 0.56 and 0.63. The correlation heatmap is shown in Figure 2. These moderate to strong correlations suggest that the symptom clusters may collectively reflect the symptom burden of the patients. Since the correlations did not exceed the high collinearity threshold of 0.8, they were treated as independent variables in the structural equation model (SEM), with the latent classes of the clusters used as observed variables. The model fit indices are shown in Table 4. Based on the path estimates from the model, the latent class (LatentClass) has a significant and strong effect on the six symptom groups. Specifically, the impact of the latent class on each symptom group ranges from 0.594 (for reflux) to 0.705 (for dyspepsia), with all path coefficients being positive, indicating a positive correlation between the latent class and the symptom groups. Furthermore, the correlations between the symptom groups suggest that they may reflect the overall symptom burden of the patients. The correlation analysis reveals that the relationships between the symptom groups range from moderate to high (between 0.39 and 1.00), with particularly high positive correlations between general symptoms and dyspepsia (0.647), and dyspepsia and reflux (0.607). These results indicate that the symptom groups not only play a role in the construction of the latent class but also that their interrelationships further reflect the overall symptom characteristics of the patients. Finally, the variance estimate for the latent class (LatentClass) is 0.329, and its effects on the symptom groups are all significant (p<0.001), demonstrating the strong explanatory power of the latent class on the symptom group scores. The distribution of the positive correlations between the symptom clusters is shown in Figure 3. 2.5 Relationship Between PRO Scale Symptom Clusters and Anxiety/Depression Scores: Artificial Neural Network Analysis The classification performance of the ANN model is summarized in Table 5. The ANN model achieved an overall accuracy of 0.7534, with a macro-average precision of 0.7558, recall of 0.755, and F1-score of 0.7528. The weighted averages were 0.7586 (precision), 0.7534 (recall), and 0.7533 (F1-score), indicating balanced performance. The ROC curve (Figure 4) showed an AUC of approximately 0.75 for all categories, suggesting good discriminative ability. Permutation vector contribution analysis (Figure 5) revealed that general symptoms (0.19), social function (0.17), and reflux (0.15) had the greatest impact on latent classification. SAS and SDS scores indirectly influenced latent classification through these symptom groups. Other symptom groups, such as bowel abnormalities (0.14), dyspepsia (0.11), and psychological emotions (0.06), had lesser but still significant impacts. The results indicated that anxiety and depression significantly impacted latent classifications, primarily through their effects on general symptoms, social function, and reflux symptom clusters. 3. Discussion Our study aimed to explore the complex relationship between psychological traits and PRO scores in chronic gastrointestinal disease patients using LCA, SEM, and ANN for a comprehensive analysis. The results identified three distinct latent classes based on symptom outcomes, highlighting significant variations in symptom dimensions across the patient population. These classifications were closely related to patients' psychological state and demographic factors, providing a personalized basis for clinical management. Unlike previous studies that primarily used regression models to assess the impact of anxiety and depression on gastrointestinal symptoms, our study offers a more nuanced perspective by demonstrating that these psychological factors influence symptom classifications indirectly through specific symptom clusters. Moreover, the integration of ANN enabled a quantitative assessment of their contribution to latent classifications, an aspect not extensively explored in prior research. These findings emphasize the necessity of incorporating psychological interventions alongside symptom management to optimize patient care. Univariate analysis revealed that patients with a high school education exhibited more severe symptoms of chronic gastrointestinal disease. This phenomenon may be related to their occupational type. Research has shown that among non-manual laborers, individuals with a high school education have a higher incidence of functional gastrointestinal disorders (FGID) and more severe symptoms. This may be due to the fact that individuals with a high school education often engage in non-manual labor, leading to higher work stress (Nagy-Szakal et al. 2017 ), which increases psychological stress and exacerbates gastrointestinal symptoms. Additionally, psychological factors play an important role in the onset and progression of chronic gastrointestinal diseases. Negative emotions such as anxiety and depression can aggravate intestinal inflammation and symptom severity, affecting treatment outcomes (Khan et al. 2024 ). Therefore, high school-educated individuals may experience more severe symptoms due to work stress and psychological factors. However, the specific cause may involve various factors such as lifestyle, socioeconomic status, etc (Lian et al. 2023 ). It is suggested that future research explore the relationship between different educational levels, occupational types, psychological states, and symptom severity in chronic gastrointestinal disease patients to develop more targeted interventions. The SEM and ANN analyses further revealed the latent structural relationships between the dimensions of the PRO scale. The SEM results showed complex causal relationships between symptom clusters, with the most significant interactions between social function and psychological/emotional symptoms, dyspepsia and reflux, and reflux and bowel irregularity. Previous studies have found that patients with anxiety and depression have a high rate of social function impairment (50%-70%) and significant correlations with gastrointestinal dysfunction (Rus Makovec et al. 2021 ). The relationship between dyspepsia and reflux is driven by gastrointestinal motility disorders, primarily manifested as delayed gastric emptying, gastric fundus relaxation disorder, or high-pressure conditions at the gastroesophageal junction (Vavricka et al. 2019). Previous studies have found that over 30% of patients with functional dyspepsia (FD) also have gastroesophageal reflux disease (GERD), with a high comorbidity rate (Li et al. 2020 ). The causal link between reflux and bowel irregularity may be related to overall gastrointestinal dysfunction and psychological factors. GERD may affect gastrointestinal motility, leading to bowel issues, and vice versa (Lee et al. 2024 ). Additionally, anxiety and depression often coexist in these diseases, potentially exacerbating symptoms and creating a vicious cycle (e.g., anxiety worsens gastrointestinal symptoms, which in turn worsens anxiety). The SEM results not only align with previous research but also demonstrate the close causal relationship of this phenomenon. The ANN analysis showed that the scores on the Self-Rating Anxiety Scale (SAS) and Self-Rating Depression Scale (SDS) have high accuracy in predicting patients' total PRO scores. Anxiety and depression are two important psychological factors that first significantly affect general symptoms, followed by other symptom clusters (e.g., gastrointestinal symptoms, psychological/emotional symptoms, and social function). The possible reason for this is that anxiety and depression are often accompanied by abnormal neuroendocrine responses. Anxiety and depression activate the hypothalamic-pituitary-adrenal (HPA) axis, leading to increased secretion of cortisol (Ball et al., 2022). Psychological stress may exacerbate functional gastrointestinal symptoms (e.g., dyspepsia, bowel irregularities) through neuroendocrine pathways such as HPA axis activation, rather than directly driving organic inflammation (Vavricka et al., 2019). Long-term elevated cortisol levels not only suppress immune function but may also contribute to systemic symptoms such as fatigue and muscle aches (Salimiyan et al., 2022 ). Anxiety and depression can also stimulate the sympathetic nervous system (the "fight or flight" response), leading to the release of adrenaline and noradrenaline, which in turn triggers general symptoms (Speers et al. 2021 ). Studies have also shown that patients with depression often exhibit elevated inflammatory markers such as C-reactive protein (CRP) and cytokines (e.g., IL-6, TNF-α) in serum (Osimo et al. 2020 ). These inflammatory factors may affect various systems in the body, leading to the aggravation of general symptoms. Animal studies have further demonstrated that chronic stress induces behavioral abnormalities through immune dysregulation (Gu et al., 2021 ). Moreover, long-term psychological stress may result in the loss of immune tolerance, leaving the body in a hypersensitive state that is more susceptible to environmental factors (Morris et al. 2022 ), further exacerbating general symptoms. On this basis, anxiety and depression, along with general symptoms, can regulate gut motility and secretion via the gut-brain axis, leading to gastrointestinal discomfort symptoms such as nausea, stomach pain, bloating, and diarrhea (Hillestad et al. 2022 ). Other studies have also shown that anxiety and depression symptoms may affect gut function by altering the gut microbiota composition, which in turn causes symptoms (Simpson et al. 2021 ). The mechanism may involve a decrease in beneficial gut bacteria and an increase in harmful bacteria, affecting normal gastrointestinal function and further exacerbating gastrointestinal symptoms. Ultimately, long-term symptoms destabilize patients' emotional states, leading to emotional dysregulation and social dysfunction. This causal chain reflects the complex interactions between symptoms in chronic gastrointestinal disease patients. Psychological factors have significant direct and indirect effects on symptoms such as dyspepsia and bowel irregularity, which is consistent with previous studies on the impact of mental health on chronic gastrointestinal diseases, providing theoretical support for future intervention strategies. This non-linear relationship complements SEM's ability to handle complex data relationships and further strengthens the understanding of the impact of psychological states on patient-reported outcomes. 4. Conclusion and Future Directions This study is the first to comprehensively utilize LCA, SEM, and ANN methods to explore the significant role of anxiety and depression (SAS and SDS scores) in exacerbating various gastrointestinal symptoms, particularly general and digestive symptoms. The results reveal the heterogeneity and complexity of symptoms in patients with chronic gastrointestinal diseases. If clinical treatment focuses solely on gastrointestinal symptoms related to the primary diagnosis, while neglecting the interactions between symptom clusters, achieving comprehensive symptom relief and improving patients' quality of life may be difficult. Therefore, clinical treatment plans should be more personalized, considering the interactions between symptom clusters and adopting a multidimensional approach to enhance effectiveness. Moreover, the study highlights the importance of early intervention in patients' mental health issues, such as anxiety and depression. This not only helps improve overall symptoms but also alleviates gastrointestinal symptoms and enhances social functioning. By integrating the management of psychological factors, systemic symptoms, and gastrointestinal symptoms, more personalized and effective treatment plans can be developed, ultimately improving patients' quality of life. The heterogeneity of the study sample (including both functional and organic diseases) may have influenced the latent classification results. Future studies should also validate these findings through subgroup analyses in more homogeneous populations. Future research should expand sample sizes and explore the interactions among additional psychological, social, and physiological factors to enhance the generalizability and reliability of the results. It is important to note that our findings are based on a sample within the Chinese educational system. Differences in educational structures across countries (e.g., vocational training pressures) may limit the generalizability of the conclusions, and cross-cultural studies are needed to validate these findings. Additionally, further studies combining biomarkers can refine predictive models and deepen the understanding of the causal relationships between anxiety, depression, and systemic symptoms. Declarations Funding This study was supported by the Key Research Project of Anhui Provincial Higher Education Institutions (2022AH050490); the Special Fund Project of the Academic Capacity Improvement Program for Integrative Chinese and Western Medicine Nursing (ZXYJHHL-K-2023-M14); the Doctoral Research Start-up Fund of Anhui University of Chinese Medicine (2025rcyb18); and the 2025 Program for the Training of Young and Middle-aged Teachers in Higher Education Institutions (JWFX2025020). The funding bodies had no role in the study design, data collection, analysis, interpretation of data, or writing of the manuscript. Ethics approval and consent to participate The study was approved by the Medical Ethics Committee of the First Affiliated Hospital of Anhui University of Chinese Medicine (Approval No. 2022MCZQ04). All procedures involving human participants were conducted in accordance with the Declaration of Helsinki and relevant institutional guidelines. Written informed consent was obtained from all participants prior to enrollment. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Availability of data and materials The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request. Authors’ contributions RW (Wang Rui) designed the study, performed statistical analyses (LCA/SEM/ANN), and drafted the manuscript. CY (Yang Caiyi), BC (Sun Baocheng), and WH (Zhang Wenhui) collected the data and assisted with data entry. SM (Chen Shimin), YX (Niu Yingxue), and M (Hu Min) contributed to data cleaning and interpretation of the results. YF (Li Yifang) supervised the study, provided critical revisions, and acted as corresponding author. All authors read and approved the final manuscript. Acknowledgements The authors sincerely thank all participants and staff involved in the study. Declaration of generative AI and AI-assisted technologies in the writing process During the preparation of this manuscript the authors used ChatGPT (OpenAI) for language editing to improve readability. The authors reviewed and edited the content as necessary and take full responsibility for the content of the publication. References Ball, J., & Darby, I. (2022). Mental health and periodontal and peri-implant diseases. Periodontology 2000 , 90(1), 106–124. https://doi.org/10.1111/prd.12452 Bisgaard, T. H., Allin, K. H., Keefer, L., Ananthakrishnan, A. N., & Jess, T. (2022). Depression and anxiety in inflammatory bowel disease: epidemiology, mechanisms and treatment. Nature reviews. Gastroenterology & hepatology , 19(11), 717–726. https://doi.org/10.1038/s41575-022-00634-6 Chinese Society of Gastroenterology, Cancer Collaboration Group of Chinese Society of Gastroenterology, Chinese Medical Association (2023). Guidelines for diagnosis and treatment of chronic gastritis in China (2022, Shanghai). Journal of digestive diseases , 24(3), 150–180. https://doi.org/10.1111/1751-2980.13193 Dahmer, M. K., Yang, G., Zhang, M., Quasney, M. W., Sapru, A., Weeks, H. M., Sinha, P., Curley, M. A. Q., Delucchi, K. L., Calfee, C. S., Flori, H., RESTORE and BALI study investigators, & Pediatric Acute Lung Injury and Sepsis Investigators (PALISI) Network (2022). Identification of phenotypes in paediatric patients with acute respiratory distress syndrome: a latent class analysis. The Lancet. Respiratory medicine , 10(3), 289–297. https://doi.org/10.1016/S2213-2600(21)00382-9 Fu, X., Yan, T., Tian, Y., Niu, X., Xu, X., Wei, Y., Hu, Q., Ouyang, Z., & Wu, X. (2022). Exploring Factors Influencing Students' Entrepreneurial Intention in Vocational Colleges Based on Structural Equation Modeling: Evidence From China. Frontiers in psychology , 13, 898319. https://doi.org/10.3389/fpsyg.2022.898319 Gu, Y., Ye, T., Tan, P., Tong, L., Ji, J., Gu, Y., Shen, Z., Shen, X., Lu, X., & Huang, C. (2021). Tolerance-inducing effect and properties of innate immune stimulation on chronic stress-induced behavioral abnormalities in mice. Brain, behavior, and immunity , 91, 451–471. https://doi.org/10.1016/j.bbi.2020.11.002 Hillestad, E. M. R., van der Meeren, A., Nagaraja, B. H., Bjørsvik, B. R., Haleem, N., Benitez-Paez, A., Sanz, Y., Hausken, T., Lied, G. A., Lundervold, A., & Berentsen, B. (2022). Gut bless you: The microbiota-gut-brain axis in irritable bowel syndrome. World journal of gastroenterology , 28(4), 412–431. https://doi.org/10.3748/wjg.v28.i4.412 Hong, Q. Q., Yan, S., Zhao, Y. L., Fan, L., Yang, L., Zhang, W. B., Liu, H., Lin, H. X., Zhang, J., Ye, Z. J., Shen, X., Cai, L. S., Zhang, G. W., Zhu, J. M., Ji, G., Chen, J. P., Wang, W., Li, Z. R., Zhu, J. T., Li, G. X., … You, J. (2024). Machine learning identifies the risk of complications after laparoscopic radical gastrectomy for gastric cancer. World journal of gastroenterology , 30(1), 79–90. https://doi.org/10.3748/wjg.v30.i1.79 Ihara, E., Manabe, N., Ohkubo, H., Ogasawara, N., Ogino, H., Kakimoto, K., Kanazawa, M., Kawahara, H., Kusano, C., Kuribayashi, S., Sawada, A., Takagi, T., Takano, S., Tomita, T., Noake, T., Hojo, M., Hokari, R., Masaoka, T., Machida, T., Misawa, N., … Higuchi, K. (2024). Evidence-Based Clinical Guidelines for Chronic Diarrhea 2023. Digestion , 105(6), 480–497. https://doi.org/10.1159/000541121 Iwakiri, K., Fujiwara, Y., Manabe, N., Ihara, E., Kuribayashi, S., Akiyama, J., Kondo, T., Yamashita, H., Ishimura, N., Kitasako, Y., Iijima, K., Koike, T., Omura, N., Nomura, T., Kawamura, O., Ohara, S., Ozawa, S., Kinoshita, Y., Mochida, S., Enomoto, N., … Koike, K. (2022). Evidence-based clinical practice guidelines for gastroesophageal reflux disease 2021. Journal of gastroenterology , 57(4), 267–285. https://doi.org/10.1007/s00535-022-01861-z Khan, S., Sebastian, S. A., Parmar, M. P., Ghadge, N., Padda, I., Keshta, A. S., Minhaz, N., & Patel, A. (2024). Factors influencing the quality of life in inflammatory bowel disease: A comprehensive review. Disease-a-month : DM , 70(1S), 101672. https://doi.org/10.1016/j.disamonth.2023.101672 Kamada, T., Satoh, K., Itoh, T., Ito, M., Iwamoto, J., Okimoto, T., Kanno, T., Sugimoto, M., Chiba, T., Nomura, S., Mieda, M., Hiraishi, H., Yoshino, J., Takagi, A., Watanabe, S., & Koike, K. (2021). Evidence-based clinical practice guidelines for peptic ulcer disease 2020. Journal of gastroenterology , 56(4), 303–322. https://doi.org/10.1007/s00535-021-01769-0 Li, J. D., Wu, J., Zhao, Y., Wang, X., Jiang, F., Hou, Q., Chen, D. N., Zheng, R., Yu, R., Zhou, W., & Men, M. (2020). Phenotypic Spectrum of Idiopathic Hypogonadotropic Hypogonadism Patients With CHD7 Variants From a Large Chinese Cohort. The Journal of clinical endocrinology and metabolism , 105(5), dgz182. https://doi.org/10.1210/clinem/dgz182 Li, X., & Jacobucci, R. (2022). Regularized structural equation modeling with stability selection. Psychological methods , 27(4), 497–518. https://doi.org/10.1037/met0000389 Sinha, P., Calfee, C. S., & Delucchi, K. L. (2021). Practitioner's Guide to Latent Class Analysis: Methodological Considerations and Common Pitfalls. Critical care medicine , 49(1), e63–e79. https://doi.org/10.1097/CCM.0000000000004710 Lian, X., Lin, Y., Peng, X., Wang, Y., He, T., He, Z., Gu, W., Wang, H., He, F., & Huang, Y. (2023). Causal links between socioeconomic status, leisure sedentary behaviours and gastro-oesophageal reflux disease: a multivariable two-sample Mendelian randomisation study. Journal of epidemiology and community health, 77(7), 460–467. https://doi.org/10.1136/jech-2023-220311 Lee, A., Kim, H. K., & Kim, H. (2024). High Prevalence and Risk Factors of Functional Gastrointestinal Disorders Among University Students in South Korea. Gastroenterology nursing : the official journal of the Society of Gastroenterology Nurses and Associates , 47(3), 195–202. https://doi.org/10.1097/SGA.0000000000000798 Miwa, H., Nagahara, A., Asakawa, A., Arai, M., Oshima, T., Kasugai, K., Kamada, K., Suzuki, H., Tanaka, F., Tominaga, K., Futagami, S., Hojo, M., Mihara, H., Higuchi, K., Kusano, M., Arisawa, T., Kato, M., Joh, T., Mochida, S., Enomoto, N., … Koike, K. (2022). Evidence-based clinical practice guidelines for functional dyspepsia 2021. Journal of gastroenterology , 57(2), 47–61. https://doi.org/10.1007/s00535-021-01843-7 Morris, G., Gevezova, M., Sarafian, V., & Maes, M. (2022). Redox regulation of the immune response. Cellular & molecular immunology , 19(10), 1079–1101. https://doi.org/10.1038/s41423-022-00902-0 Nagy-Szakal, D., Williams, B. L., Mishra, N., Che, X., Lee, B., Bateman, L., Klimas, N. G., Komaroff, A. L., Levine, S., Montoya, J. G., Peterson, D. L., Ramanan, D., Jain, K., Eddy, M. L., Hornig, M., & Lipkin, W. I. (2017). Fecal metagenomic profiles in subgroups of patients with myalgic encephalomyelitis/chronic fatigue syndrome. Microbiome , 5(1), 44. https://doi.org/10.1186/s40168-017-0261-y Osimo, E. F., Pillinger, T., Rodriguez, I. M., Khandaker, G. M., Pariante, C. M., & Howes, O. D. (2020). Inflammatory markers in depression: A meta-analysis of mean differences and variability in 5,166 patients and 5,083 controls. Brain, behavior, and immunity , 87, 901–909. https://doi.org/10.1016/j.bbi.2020.02.010 Rubin, D. T., Ananthakrishnan, A. N., Siegel, C. A., Sauer, B. G., & Long, M. D. (2019). ACG Clinical Guideline: Ulcerative Colitis in Adults. The American journal of gastroenterology, 114(3), 384–413. https://doi.org/10.14309/ajg.0000000000000152 Rus Makovec, M., Vintar, N., & Makovec, S. (2021). Level of Depression, Anxiety and Impairment of Social Relations with Regard to Pain Intensity in a Naturalistic Sample of Patients at the Outpatient Chronic Pain Clinic. Psychiatria Danubina , 33(Suppl 4), 558–564. Salimiyan, S., Mohammadi, M., Aliakbari, S., Kazemi, R., Amini, A. A., & Rahmani, M. R. (2022). Hydrocortisone Long-term Treatment Effect on Immunomodulatory Properties of Human Adipose-Derived Mesenchymal Stromal/Stem Cells. Journal of interferon & cytokine research : the official journal of the International Society for Interferon and Cytokine Research , 42(2), 72–81. https://doi.org/10.1089/jir.2021.0120 Simpson, C. A., Diaz-Arteche, C., Eliby, D., Schwartz, O. S., Simmons, J. G., & Cowan, C. S. M. (2021). The gut microbiota in anxiety and depression - A systematic review. Clinical psychology review , 83, 101943. https://doi.org/10.1016/j.cpr.2020.101943 Speers, A. B., Cabey, K. A., Soumyanath, A., & Wright, K. M. (2021). Effects of Withania somnifera (Ashwagandha) on Stress and the Stress- Related Neuropsychiatric Disorders Anxiety, Depression, and Insomnia. Current neuropharmacology , 19(9), 1468–1495. https://doi.org/10.2174/1570159X19666210712151556 Tanaka, S., Saitoh, Y., Matsuda, T., Igarashi, M., Matsumoto, T., Iwao, Y., Suzuki, Y., Nozaki, R., Sugai, T., Oka, S., Itabashi, M., Sugihara, K. I., Tsuruta, O., Hirata, I., Nishida, H., Miwa, H., Enomoto, N., Shimosegawa, T., & Koike, K. (2021). Evidence-based clinical practice guidelines for management of colorectal polyps. Journal of gastroenterology , 56(4), 323–335. https://doi.org/10.1007/s00535-021-01776-1 Tang, X., Wang, P., Zhao, Y., et al. (2018). Experts questionnaires survey on the applicability for FGIDs of patient reported outcomes instrument for chronic gastrointestinal diseases. Chinese Journal of Integrative Medicine , 38(2), 182-185. Tiksnadi, B. B., Triani, N., Fihaya, F. Y., Turu' Allo, I. J., Iskandar, S., & Putri, D. A. E. (2023). Validation of Hospital Anxiety and Depression Scale in an Indonesian population: a scale adaptation study. Family medicine and community health , 11(2), e001775. https://doi.org/10.1136/fmch-2022-001775 Vavricka, S. R., & Greuter, T. (2019). Gastroparesis and Dumping Syndrome: Current Concepts and Management. Journal of clinical medicine , 8(8), 1127. https://doi.org/10.3390/jcm8081127 Warren, A., Nyavor, Y., Beguelin, A., & Frame, L. A. (2024). Dangers of the chronic stress response in the context of the microbiota-gut-immune-brain axis and mental health: a narrative review. Frontiers in immunology , 15, 1365871. https://doi.org/10.3389/fimmu.2024.1365871 Zhang, J., Yu, P., Xu, Y., Lu, X. Y., Xu, Y., Hang, J., & Zhang, Y. (2024). Efficacy and Safety of a Low-FODMAP Diet in Combination with a Gluten-Free Diet for Adult Irritable Bowel Syndrome: A Systematic Review and Meta-Analysis. Digestive diseases and sciences , 69(11), 4124–4132. https://doi.org/10.1007/s10620-024-08671-8 Tables Table 1 Distribution of Symptom Cluster Scores and Total PRO Scores general symptoms dyspepsia reflux abnormal bowel movements psychological factors social factors Total PRO Score Category 1 5.37 ± 3.56 6.63 ± 4.05 3.27 ± 3.16 5.62 ± 3.73 2.59 ± 1.84 1.24 ± 0.81 24.47 ± 9.89 Category2 11.17 ± 3.70 14.93 ± 5.74 8.66 ± 5.34 12.62 ± 5.51 5.28 ± 2.35 3.05 ± 2.29 50.77 ± 12.99 Category3 18.2 ± 2.42 26.30 ± 6.78 16.31 ± 5.60 22.46 ± 6.96 10.31 ± 1.70 8.84 ± 2.08 78.52 ± 16.17 Table 2 Results of Latent Classification Model AIC BIC aBIC Entropy P Group size (%) 1 3559.63 3603.66 3603.75 - - 1 2 3214.96 3316.56 3306.88 0.14 0.00 0.4655/0.5345 3 3177.11 3306.71 3316.82 0.14 0.00 0.3379/0.2828/0.3793 4 3175.62 3362.79 3363.14 0.40 0.01 0.2793/0.2276/0.331/0.1621 5 3174.24 3409.11 3409.55 0.27 0.01 0.0586/0.3724/0.2276/0.2621/0.0793 Table 3 General Demographic Characteristics Characteristics N Category 1 Category 2 Category 3 χ 2 P Gender Male 132 42 57 33 2.960 0.228 Female 158 56 53 49 Age 18–45 79 27 31 21 3.068 0.546 45–60 120 44 39 37 >60 91 27 40 24 Residence Urban 124 46 45 33 1.065 0.587 Rural 166 52 65 49 Marital Unmarried 20 7 6 7 1.770 0.778 Status Married 256 88 98 70 Divorced 14 3 6 5 Educational Below High School 57 16 22 19 11.740 0.019 Level High School 134 44 43 47 Postgraduate or Higher 99 38 45 16 Treatment History No 109 40 43 26 1.750 0.417 Yes 181 58 67 56 Monthly Income 8000 35 16 13 6 Occupation Student 14 7 2 5 5.736 0.677 Farmer 87 30 34 23 Clerk / Staff 80 24 34 22 Retired 78 26 31 21 Others 31 11 9 11 Duration 5 Years 71 16 38 17 Table 4 Results of Path Analysis Using Structural Equation Modeling Path Estimate Std.Err Z-value P-value Std.lv Std.all Latent Class~ general symptoms 1.989 0.204 9.725 0.000 1.989 0.696 dyspepsia 2.921 0.298 9.814 0.000 2.921 0.705 reflux 1.913 0.220 8.698 0.004 1.913 0.594 abnormal bowel movements 2.482 0.264 9.415 0.000 2.482 0.663 psychological factors 1.031 0.112 9.210 0.000 1.031 0.643 social factors 0.960 0.104 9.227 0.000 0.960 0.645 general symptoms~ dyspepsia 23.294 2.518 9.251 0.000 23.294 0.647 reflux 15.045 1.866 8.065 0.000 15.045 0.538 abnormal bowel movements 16.898 2.152 7.854 0.000 16.898 0.520 psychological factors 7.427 0.927 8.012 0.000 7.427 0.533 social factors 6.610 0.853 7.749 0.000 6.610 0.511 dyspepsia ~ reflux 24.599 2.785 8.832 0.000 0.400 0.400 abnormal bowel movements 25.170 3.137 8.024 0.000 0.215 0.215 psychological factors 9.662 1.314 7.352 0.000 0.145 0.145 social factors 9.307 1.229 7.572 0.000 0.135 0.135 reflux ~ abnormal bowel movements 16.690 2.363 7.062 0.000 16.690 0.456 psychological factors 6.176 0.990 7.062 0.000 6.176 0.394 social factors 5.967 0.925 6.453 0.000 5.967 0.409 abnormal bowel movements ~ psychological factors 8.502 1.181 7.197 0.000 8.502 0.466 social factors 9.150 1.130 8.097 0.000 9.150 0.540 psychological factors ~ social factors 4.244 0.494 8.600 0.000 4.244 0.585 Table 5 Classification Evaluation Report of ANN Category Precision Recall F1-Score Support Category 1 0.7083 0.7727 0.7391 22 Category 2 0.8182 0.6923 0.75 26 Category 3 0.7407 0.8 0.7692 25 Accuracy 0.7534 - - 73 Macro avg 0.7558 0.755 0.7528 73 Weighted avg 0.7586 0.7534 0.7533 73 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 14 Jan, 2026 Editor invited by journal 17 Dec, 2025 Editor assigned by journal 14 Dec, 2025 Submission checks completed at journal 14 Dec, 2025 First submitted to journal 14 Dec, 2025 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8357816","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":575016513,"identity":"67026822-7aed-454d-9480-9285a9199674","order_by":0,"name":"Wang Rui","email":"","orcid":"","institution":"The First Affiliated Hospital of Anhui University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Wang","middleName":"","lastName":"Rui","suffix":""},{"id":575016514,"identity":"7ff09922-1b07-463d-9a44-2bd6eec1de80","order_by":1,"name":"Caiyi Yang","email":"","orcid":"","institution":"The First Affiliated Hospital of Anhui University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Caiyi","middleName":"","lastName":"Yang","suffix":""},{"id":575016515,"identity":"f79daea4-5521-4dc6-938d-e770033f89a7","order_by":2,"name":"Baocheng Sun","email":"","orcid":"","institution":"The First Affiliated Hospital of Anhui University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Baocheng","middleName":"","lastName":"Sun","suffix":""},{"id":575016516,"identity":"561a0f1d-1d70-4a04-b3ff-85c3948f45c9","order_by":3,"name":"Wenhui Zhang","email":"","orcid":"","institution":"The First Affiliated Hospital of Anhui University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Wenhui","middleName":"","lastName":"Zhang","suffix":""},{"id":575016517,"identity":"9d6eb875-8b8f-4b5b-8b14-307c4d781ff3","order_by":4,"name":"Shimin Chen","email":"","orcid":"","institution":"Anhui University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Shimin","middleName":"","lastName":"Chen","suffix":""},{"id":575016518,"identity":"046dfa77-126d-4142-a2c8-d40fc9f15246","order_by":5,"name":"Yingxue Niu","email":"","orcid":"","institution":"Anhui University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yingxue","middleName":"","lastName":"Niu","suffix":""},{"id":575016519,"identity":"29638642-3530-4a9e-a5fe-e713de1c5ef2","order_by":6,"name":"Min Hu","email":"","orcid":"","institution":"Anhui University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Hu","suffix":""},{"id":575016520,"identity":"9035cf05-be8f-4168-90c5-5d6a0374be19","order_by":7,"name":"Yifang Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYBACPmYGhgMVQAY/Aw9U6AABLWwgLWeADMkGHsYG4rSACJAWgwNEa2HnMTxwoOaO3ebjZ48/+JnDIMd3I4HxcwFeh/EYHDhw7FnytjN5iY292xiMJW8kMEvPIKDl8Ae2w8lmB3IMmxm3MSRuuJEAFCRoy7/Dycb9b8Ba6onTcrDtsJ2BBMSWBAPCWtgKDhzsO5wgceON4czebRKGM888bJbGp4Wf//DmDwe+Hbbn788x+PBzm4083/Hkg5/xaWFg4DAAkYkNEJ4EEEPjBzdgfwAi7QmoGgWjYBSMgpEMAFUqUXbsmOuRAAAAAElFTkSuQmCC","orcid":"","institution":"Anhui University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Yifang","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-12-14 12:08:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8357816/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8357816/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100594977,"identity":"d3463790-82b4-490b-b709-f97eaa9f2b12","added_by":"auto","created_at":"2026-01-19 13:46:48","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":286782,"visible":true,"origin":"","legend":"","description":"","filename":"LatentClassificationandAssociationsBetweenPsy.docx","url":"https://assets-eu.researchsquare.com/files/rs-8357816/v1/2f00ae90a7a97489e7caf7ee.docx"},{"id":100567406,"identity":"c4bdb7f5-3a1a-4276-8f5b-d996e5b276c0","added_by":"auto","created_at":"2026-01-19 09:11:46","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9163,"visible":true,"origin":"","legend":"","description":"","filename":"f72dab7a4ecc4501b866f69dacfe8a58.json","url":"https://assets-eu.researchsquare.com/files/rs-8357816/v1/d1e83a54ba28b307ddc52976.json"},{"id":100595151,"identity":"2aad296b-1ed1-4dad-bb0e-b21eca5d3310","added_by":"auto","created_at":"2026-01-19 13:47:41","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":157298,"visible":true,"origin":"","legend":"","description":"","filename":"f72dab7a4ecc4501b866f69dacfe8a581enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8357816/v1/c40a97ba83eb3317db608001.xml"},{"id":100567412,"identity":"a47e6151-d7bd-40a9-bad6-08e0ed930100","added_by":"auto","created_at":"2026-01-19 09:11:46","extension":"emf","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":180140,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.emf","url":"https://assets-eu.researchsquare.com/files/rs-8357816/v1/597d1e58be87692a980cf9c1.emf"},{"id":100595215,"identity":"18ff5fee-5368-4c44-b6d5-c7a84f04a003","added_by":"auto","created_at":"2026-01-19 13:47:55","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":31247,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8357816/v1/d365b6bda4d5c3c536d0e704.png"},{"id":100595475,"identity":"8de2a71a-bcfd-455d-9f11-5aa2909fb159","added_by":"auto","created_at":"2026-01-19 13:48:33","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":133215,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8357816/v1/66a4704241c049bc237b2a93.png"},{"id":100567416,"identity":"155c11db-504a-41ad-b2d3-3bf8a3c4814b","added_by":"auto","created_at":"2026-01-19 09:11:46","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":32798,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8357816/v1/b08e98c993719f4c88f918c4.png"},{"id":100594751,"identity":"a44b81eb-1085-4705-ac54-3fda0ef29097","added_by":"auto","created_at":"2026-01-19 13:44:45","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":25592,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8357816/v1/c3b45e17ce0214bda475f563.png"},{"id":100567415,"identity":"b5e1ba99-134f-4ecd-b0ad-824a019f173d","added_by":"auto","created_at":"2026-01-19 09:11:46","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":22582,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8357816/v1/cb88d268f882d62b78d21391.png"},{"id":100595512,"identity":"a8598890-d6ac-4a17-9094-d3d2658bf4f8","added_by":"auto","created_at":"2026-01-19 13:48:39","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":13339,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8357816/v1/254a3ca40482b15c185aeb53.png"},{"id":100567420,"identity":"deec197a-66c1-4673-bb39-65da5ac3e810","added_by":"auto","created_at":"2026-01-19 09:11:46","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":37045,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8357816/v1/66c399f0352c987f2a9ea9fd.png"},{"id":100567418,"identity":"37ee1b4e-9888-4aa2-a1c1-764f62b5aaf5","added_by":"auto","created_at":"2026-01-19 09:11:46","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":14981,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8357816/v1/c8d19c7ec2f4a3a184eee46e.png"},{"id":100567419,"identity":"3df31601-0987-460e-aff6-7aaaf7848357","added_by":"auto","created_at":"2026-01-19 09:11:46","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7563,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8357816/v1/60c71025f610be948fbe8a8d.png"},{"id":100567421,"identity":"78931aad-c197-4f49-93bd-ee9e2611bd93","added_by":"auto","created_at":"2026-01-19 09:11:46","extension":"xml","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":154487,"visible":true,"origin":"","legend":"","description":"","filename":"f72dab7a4ecc4501b866f69dacfe8a581structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8357816/v1/9c34717964edbaf1605f211e.xml"},{"id":100567423,"identity":"d32b5c2d-fb22-4e23-b011-a2ad3370467c","added_by":"auto","created_at":"2026-01-19 09:11:46","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":165436,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8357816/v1/97663e805b0b14f8c835f067.html"},{"id":100567404,"identity":"81f2f811-ded9-4869-a14a-019e51dd911d","added_by":"auto","created_at":"2026-01-19 09:11:46","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":79731,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of 39 Symptoms across 6 Symptom Clusters in the 3 Categories\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8357816/v1/16b860b53af55b770d43688e.jpg"},{"id":100567405,"identity":"d830f337-7328-42a8-9927-d43053e42239","added_by":"auto","created_at":"2026-01-19 09:11:46","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":63389,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation Heatmap\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8357816/v1/cbb9c66f6d0c4d490bd61086.jpg"},{"id":100567403,"identity":"0621b70a-5db2-490a-8907-9bd0a2a4bc04","added_by":"auto","created_at":"2026-01-19 09:11:46","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":81236,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSEM Path Diagram\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8357816/v1/c3580ac61bc24ccb9b18e3ac.jpg"},{"id":100567408,"identity":"63790aa6-9109-4400-95fa-69ff91ff924f","added_by":"auto","created_at":"2026-01-19 09:11:46","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":45306,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curve (One-vs-Rest multiclass )\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8357816/v1/34a6ff5b005e52e7bb50c020.jpg"},{"id":100567411,"identity":"e45dba9c-2095-4486-88b2-cb83bbfb8b7a","added_by":"auto","created_at":"2026-01-19 09:11:46","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":48235,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePermutation Importance\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8357816/v1/ffb223b1f01a8bbe4908375e.jpg"},{"id":101397583,"identity":"aeab4f53-3f92-4e67-961a-83e5e0a00851","added_by":"auto","created_at":"2026-01-29 09:30:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1682911,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8357816/v1/ccd92703-5966-4e31-ba97-91174c8a683c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Latent Classification and Psychological Associations with Quality of Life in Chronic Gastrointestinal Disease Patients: A Cross-Sectional Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWith the in-depth study of the relationship between mental health and physical diseases, the important role of psychological factors in chronic diseases has gained increasing attention. Common psychological states, such as anxiety and depression, are not only core issues in the field of mental health but also play a key role in the onset and progression of many physical diseases (Bisgaard et al., 2022). Particularly in chronic gastrointestinal diseases, psychological factors influence patients' clinical symptoms, disease progression, and quality of life through a complex neuro-endocrine-immune regulatory network, gradually becoming a hot topic in interdisciplinary research (Warren et al., 2024).\u003c/p\u003e\n\u003cp\u003ePatient reported outcomes instrument for chronic gastrointestinal diseases (PRO-GI) scale, as an important tool reflecting the patient's subjective health status and quality of life, has been widely used in the research and management of chronic gastrointestinal diseases (Tang et al., 2018). The selection of dimensions such as dyspepsia, reflux, and abnormal bowel movements was driven by the need to reflect the complexity of symptom clusters rather than isolated symptoms (e.g., abdominal pain). The PRO-GI scale encompasses six dimensions: general symptoms, dyspepsia, reflux, abnormal bowel movements, psychological factors, and social factors, thereby capturing interactions between symptom groups and psychological traits. This multidimensional approach allows for the prediction of disease progression and provides a foundation for personalized interventions. The scale contains 39 questions, including 6 on general symptoms, 10 on dyspepsia, 8 on reflux symptoms, 9 on abnormal bowel movements, and 3 each on psychological and social functioning issues. By assessing these dimensions, the PRO scale can reveal patients' symptom heterogeneity and the association with mental health, providing a basis for personalized treatment plans.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, most existing studies are limited to single statistical analysis methods, making it difficult to comprehensively reveal the complex relationship between PRO, psychological characteristics, and demographic factors. Multivariate statistical methods, such as Latent Class Analysis (LCA), can provide detailed classification of patient populations, but have limitations in exploring the complex relationships between variables (Sinha et al. 2021). Structural Equation Modeling (SEM), as a multidimensional correlation analysis tool, can intuitively display the path relationships between latent variables and their impact mechanisms(Li \u0026amp; Jacobucci, 2022; Fu et al., 2022). Machine learning methods, such as Artificial Neural Networks (ANN), have significant advantages in handling nonlinear data relationships (Hong et al. 2024). To our knowledge, this is the first study to integrate LCA, SEM, and ANN, overcoming the limitations of traditional single-method approaches. This multimodal framework provides a multidimensional perspective on symptom-psychological interactions, enabling both classification and mechanistic exploration of heterogeneous patient groups.\u0026nbsp;Combining these methods offers an innovative strategy for analyzing the latent classification characteristics of the PRO scale and its association with psychological traits. This study aims to utilize the advantages of statistical methods and machine learning by combining LCA for latent classification of patient groups, analyzing path relationships between latent variables through SEM, and introducing ANN to further explore the complex interaction mechanisms between the PRO scale, psychological characteristics, and demographic factors, with the expectation of providing more precise theoretical guidance for clinical interventions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Subjects and Methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo provide a more comprehensive understanding of symptom clustering and its underlying mechanisms, we employed a combination of LCA, SEM, and ANN. While LCA identified distinct patient subgroups based on symptom presentation, SEM elucidated the relationships among symptom clusters, and ANN quantified the contribution of psychological factors to latent classifications.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.1. Study Subjects\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study selected chronic gastrointestinal disease patients admitted to the First Affiliated Hospital of Anhui University of Chinese Medicine from March 2022 to March 2023 as the research subjects.\u003c/p\u003e\n\u003cp\u003eInclusion Criteria: ①Clinically diagnosed with chronic gastritis, including chronic non-atrophic gastritis, chronic atrophic gastritis, and special types of gastritis (Chinese Society of Gastroenterology et al. 2023), peptic ulcer (Kamada et al. 2021), gastroesophageal reflux disease (Iwakiri et al. 2022), functional dyspepsia (Miwa et al. 2022), chronic diarrhea (Ihara et al. 2024), ulcerative colitis (Rubin et al. 2019), gastric polyps and intestinal polyps (Tanaka et al. 2021). The inclusion of both functional and organic gastrointestinal diseases aimed to reflect real-world clinical diversity. The diagnoses of the above diseases are based on guidelines or expert consensus. ②Aged 18 years or older. ③Literate at the elementary school level or above, capable of reading and understanding the questionnaire. ④Disease duration of at least 1 month.\u003c/p\u003e\n\u003cp\u003eExclusion Criteria: ①Presence of other organic diseases requiring treatment. ②Inconsistent or superficial responses, where the scale results do not align with the patient's usual performance.\u003c/p\u003e\n\u003cp\u003eThis study used SEM to analyze the 6 dimensions of the Chronic Gastrointestinal Disease Patient-Reported Outcomes PRO scale. According to the rule of thumb, each model parameter that needs to be estimated requires at least 20 samples, thus a total of 120 samples were needed. ANN were employed to investigate the latent effects of the Self-Rating Anxiety Scale (SAS) and Self-Rating Depression Scale (SDS) on PRO. Based on the Baum-Haussler formula, 284 samples were needed for a 95% confidence interval. The study was approved by the Ethics Committee of the First Affiliated Hospital of Anhui University of Chinese Medicine (Approval No. 2022MCZQ04).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.2 Research Content\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.2.1 General Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis includes demographic characteristics and disease-related information. The demographic characteristics include gender, age, education level, marital status, employment status, monthly income, etc. The disease-related information includes the disease diagnosis, duration, medication history, etc.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.2.2 Patient-Reported Outcome Measures for Chronic Gastrointestinal Diseases\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe patient reported outcomes instrument for chronic gastrointestinal diseases (PRO-GI) is a scale specifically used to assess chronic gastrointestinal disease patients' symptoms, functional status, health-related quality of life (HRQoL), and other aspects of their subjective experience and clinical outcomes.\u0026nbsp;Developed to capture the complexity of symptom clusters in chronic gastrointestinal diseases\u0026nbsp;(Tang et al., 2018), the PRO-GI scale comprises 39 items classified into six dimensions: systemic symptoms, dyspepsia, reflux, abnormal bowel movements, psychological factors, and social factors.\u0026nbsp;This multidimensional structure was validated in prior studies, demonstrating high reliability (Cronbach’s α = 0.85-0.93). Because gastrointestinal disease symptoms often overlap, the PRO-GI provides a holistic assessment of treatment outcomes across symptomatology, satisfaction, and quality of life. For each group of symptoms, responses are scored based on frequency: 0 points for \"none,\" 1 point for \"occasionally,\" 2 points for \"sometimes,\" 3 points for \"often,\" and 4 points for \"always.\" Scores are calculated for each symptom group and a total score. The Cronbach’s α coefficient for this scale is 0.85, and in this study, the Cronbach’s α coefficient is 0.93.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.2.3 Self-Rating Anxiety Scale and Self-Rating Depression Scale\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Self-Rating Anxiety Scale (SAS) and Self-Rating Depression Scale (SDS) are self-report scales widely used in psychological health assessment (Zhang et al. 2024), developed by Zung. The SAS contains 20 items and is mainly used to assess the severity of anxiety symptoms, covering physical, emotional, and cognitive aspects. The score is calculated by multiplying the total score by 1.25 to obtain the standardized score. A score ≥50 indicates the presence of anxiety. The Cronbach's α coefficient of this scale is 0.91 (Tiksnadi et al. 2023), and in this study, the Cronbach's α coefficient is 0.82. Similarly, the SDS also includes 20 items and mainly assesses depression symptoms, covering both psychological and physical aspects. The scoring method is the same as that of the SAS, and a standardized score ≥53 suggests the possible presence of depression. The Cronbach's α coefficient of this scale is 0.87 (Tiksnadi et al. 2023), and in this study, the Cronbach's α coefficient is 0.85. In this study, SAS and SDS were used to assess patients' anxiety and depression status, providing a reference for the analysis of disease-related psychological factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3 Data Collection and Quality Control Methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll patient questionnaire data were collected in the hospital, with the researcher distributing the questionnaires face-to-face. Before the survey, the purpose and significance of the study were explained to the patients, and their consent was obtained before distributing the questionnaires. Patients were instructed to fill out the questionnaires independently. During the completion process, unified instructions were provided to explain how to fill out the questionnaires and answer any questions from patients. Disease-related information was collected by the researcher through electronic medical records or by inquiring with the patients. Once the questionnaires were completed, they were collected on-site, and the researcher checked the completion status. If any information was missing, patients were asked to complete it immediately. If there were patterns in the responses, or if all options were identical or the patient refused to answer additional questions, the questionnaire was deemed invalid.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.4 Statistical Methods\u003cbr\u003e\u003c/strong\u003eAll data were entered by two researchers and cross-checked to ensure accuracy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalytical Tools:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLatent Class Analysis (LCA)\u0026nbsp;was performed using the\u0026nbsp;poLCA\u0026nbsp;package in\u0026nbsp;R (version 4.3.1)\u0026nbsp;to identify the optimal latent classification of symptom clusters.\u003c/p\u003e\n\u003cp\u003eDescriptive statistics (e.g., means, standard deviations) and hypothesis testing (e.g., ANOVA, χ² tests) were conducted using SPSS 26.0 (IBM Corp., Armonk, NY, USA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.4.1 Latent Class Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis began with a single-class model, gradually increasing the number of classes until the model's fit indices reached optimal values. Fit indices include:\u003cbr\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;① Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and sample size-adjusted BIC (aBIC), with lower values indicating better model fit;\u003cbr\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;② Entropy, ranging from 0 to 1, with values closer to 1 indicating more accurate classification;\u003cbr\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;③ Bootstrap likelihood ratio test (BLRT), where a p-value \u0026lt; 0.05 indicates that the k-class model fits better than the k-1 class model (Dahmer et al. 2022). Finally, discriminant analysis was performed to verify the accuracy of the optimal model obtained from the latent profile analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.4.2 Comprehensive Data Analysis Methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on Latent Class Analysis (LCA), Structural Equation Modeling (SEM), and Artificial Neural Networks (ANN), the data were analyzed to explore the interrelationships and potential influencing factors of different symptom clusters in chronic gastrointestinal disease patients. First, SEM was applied to assess the latent structural relationships of the scale. The six symptom clusters from the PRO scale were used as latent variables, and the factor loadings and path coefficients of the observed indicators were examined to validate the associations and causal relationships between different symptom clusters in chronic gastrointestinal diseases.\u003c/p\u003e\n\u003cp\u003eNext, an ANN model was applied to further analyze the data from the PRO, SAS, and SDS scales to predict the impact of external states, such as anxiety and depression, on the severity of symptoms in chronic gastrointestinal diseases. The six symptom clusters of the PRO scale were used as continuous predictors, with the total scores of the SAS and SDS as factor variables, and the latent classification of the PRO scale was set as the outcome. Model performance was evaluated using accuracy, precision, recall, and F1-score to predict the latent classification based on symptom clusters and anxiety/depression scores.\u003c/p\u003e\n\u003cp\u003eThe results from this comprehensive analysis highlight the complex relationship between psychological factors and gastrointestinal symptoms, offering deeper insights for targeted interventions in chronic gastrointestinal disease management.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.4.3 Statistical Processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSPSS 26.0 statistical software was used for data analysis. Normally distributed continuous data were expressed as means ± standard deviations, and comparisons among multiple groups were performed using one-way analysis of variance (ANOVA). Count data were presented as frequencies and percentages, with comparisons between multiple groups using the χ² test or Fisher’s exact test. Multivariate logistic regression was used to further explore the factors influencing IBD patients' fear of disease progression. A p-value \u0026lt; 0.05 was considered statistically significant for all tests.\u003c/p\u003e"},{"header":"2. Results","content":"\u003cp\u003e\u003cstrong\u003e2.1 General Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 304 patients were included in this study, with 14 invalid questionnaires excluded. After data cleaning, 290 valid questionnaires were collected, resulting in a response rate of 95.39%. The PRO scores for each symptom cluster and total score are shown in Table 1. The distribution of general information for symptom clusters is presented in Table 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Common Method Bias Test Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of the common method bias test show that among the 39 questions of the PRO scale, there are 10 factors with eigenvalues \u0026gt; 1. The first principal component explained 29.85% of the variance (less than 40%), indicating that there is no significant common method bias.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Latent Classification Results of PRO Scale in Chronic Gastrointestinal Disease Patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the average scores of the 6 symptom clusters of the Chronic Gastrointestinal Disease Patient Reported Outcomes (PRO), latent class models were fit. Starting from the initial model, the number of categories was gradually increased, establishing latent class models for 1 to 5 categories (Table 2). With an increase in the number of categories, AIC, BIC, and aBIC showed an overall decreasing trend. The BIC and aBIC were minimized in the 3-class model. The entropy value was around 0.1, indicating that the data structure was well-defined. The results suggest that the 3-class model (low/medium/high) is optimal and reliable. Based on the 3-class model, the mean scores for the PRO total score and each symptom cluster are presented in Figure 1. The three categories were named according to the fluctuation in mean item scores.\u003c/p\u003e\n\u003cp\u003eCategory 1 (C1) had a PRO total score of (21.26 ± 8.11), with a mean item score of 0.6, corresponding to the options \"None\" and \"Occasionally.\" This group represents patients who can adapt to the impact of the disease on health and social aspects, thus named the \"Low Risk-Disease Adaptation Group\" (33.79% of patients). Category 2 (C2) had a PRO total score of (44.15 ± 11.11), with a mean item score of 1.3, corresponding to the options \"Sometimes\" and \"Frequently,\" and was named the \"Medium Risk-Disease Distress Group\" (37.93%). Category 3 (C3) had a PRO total score of (70.79 ± 18.63), with a mean item score of 2.1, corresponding to the option \"Always,\" and was named the \"High Risk-Functional Impairment Group\" (28.28%). The distribution of scores is shown in Figure 1.\u003c/p\u003e\n\u003cp\u003eThe univariate analysis based on latent classification showed no significant differences among the three latent categories in gender, age, residence, marital status, monthly income, occupation, and treatment history (P \u0026gt; 0.05). However, significant differences were observed in disease duration and education level. Longer disease duration was associated with higher symptom scores, indicating a positive correlation between disease duration and symptom severity. Regarding education, high school graduates were more likely to be in category 3 (35.07%), while those with lower than high school education showed an even distribution across categories. In contrast, individuals with a bachelor's degree or higher were predominantly in category 2 (45.45%). Detailed results are in Table 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Causal Structure Between PRO Scale Symptom Clusters in Chronic Gastrointestinal Diseases: SEM Analysis and Regression Coefficients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Pearson correlation analysis showed that the six symptom clusters had correlations ranging from 0.39 to 1.00, with means between 0.56 and 0.63. The correlation heatmap is shown in Figure 2. These moderate to strong correlations suggest that the symptom clusters may collectively reflect the symptom burden of the patients. Since the correlations did not exceed the high collinearity threshold of 0.8, they were treated as independent variables in the structural equation model (SEM), with the latent classes of the clusters used as observed variables. The model fit indices are shown in Table 4.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBased on the path estimates from the model, the latent class (LatentClass) has a significant and strong effect on the six symptom groups. Specifically, the impact of the latent class on each symptom group ranges from 0.594 (for reflux) to 0.705 (for dyspepsia), with all path coefficients being positive, indicating a positive correlation between the latent class and the symptom groups. Furthermore, the correlations between the symptom groups suggest that they may reflect the overall symptom burden of the patients. The correlation analysis reveals that the relationships between the symptom groups range from moderate to high (between 0.39 and 1.00), with particularly high positive correlations between general symptoms and dyspepsia (0.647), and dyspepsia and reflux (0.607). These results indicate that the symptom groups not only play a role in the construction of the latent class but also that their interrelationships further reflect the overall symptom characteristics of the patients.\u003c/p\u003e\n\u003cp\u003eFinally, the variance estimate for the latent class (LatentClass) is 0.329, and its effects on the symptom groups are all significant (p\u0026lt;0.001), demonstrating the strong explanatory power of the latent class on the symptom group scores. The distribution of the positive correlations between the symptom clusters is shown in Figure 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Relationship Between PRO Scale Symptom Clusters and Anxiety/Depression Scores: Artificial Neural Network Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe classification performance of the ANN model is summarized in Table 5.\u0026nbsp;The ANN model achieved an overall accuracy of 0.7534, with a macro-average precision of 0.7558, recall of 0.755, and F1-score of 0.7528. The weighted averages were 0.7586 (precision), 0.7534 (recall), and 0.7533 (F1-score), indicating balanced performance. The ROC curve (Figure 4) showed an AUC of approximately 0.75 for all categories, suggesting good discriminative ability.\u003c/p\u003e\n\u003cp\u003ePermutation vector contribution analysis (Figure 5) revealed that general symptoms (0.19), social function (0.17), and reflux (0.15) had the greatest impact on latent classification. SAS and SDS scores indirectly influenced latent classification through these symptom groups. Other symptom groups, such as bowel abnormalities (0.14), dyspepsia (0.11), and psychological emotions (0.06), had lesser but still significant impacts. The results indicated that anxiety and depression significantly impacted latent classifications, primarily through their effects on general symptoms, social function, and reflux symptom clusters.\u003c/p\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eOur study aimed to explore the complex relationship between psychological traits and PRO scores in chronic gastrointestinal disease patients using LCA, SEM, and ANN for a comprehensive analysis. The results identified three distinct latent classes based on symptom outcomes, highlighting significant variations in symptom dimensions across the patient population. These classifications were closely related to patients' psychological state and demographic factors, providing a personalized basis for clinical management.\u003c/p\u003e \u003cp\u003eUnlike previous studies that primarily used regression models to assess the impact of anxiety and depression on gastrointestinal symptoms, our study offers a more nuanced perspective by demonstrating that these psychological factors influence symptom classifications indirectly through specific symptom clusters. Moreover, the integration of ANN enabled a quantitative assessment of their contribution to latent classifications, an aspect not extensively explored in prior research. These findings emphasize the necessity of incorporating psychological interventions alongside symptom management to optimize patient care.\u003c/p\u003e \u003cp\u003eUnivariate analysis revealed that patients with a high school education exhibited more severe symptoms of chronic gastrointestinal disease. This phenomenon may be related to their occupational type. Research has shown that among non-manual laborers, individuals with a high school education have a higher incidence of functional gastrointestinal disorders (FGID) and more severe symptoms. This may be due to the fact that individuals with a high school education often engage in non-manual labor, leading to higher work stress (Nagy-Szakal et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), which increases psychological stress and exacerbates gastrointestinal symptoms. Additionally, psychological factors play an important role in the onset and progression of chronic gastrointestinal diseases. Negative emotions such as anxiety and depression can aggravate intestinal inflammation and symptom severity, affecting treatment outcomes (Khan et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, high school-educated individuals may experience more severe symptoms due to work stress and psychological factors. However, the specific cause may involve various factors such as lifestyle, socioeconomic status, etc (Lian et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It is suggested that future research explore the relationship between different educational levels, occupational types, psychological states, and symptom severity in chronic gastrointestinal disease patients to develop more targeted interventions.\u003c/p\u003e \u003cp\u003eThe SEM and ANN analyses further revealed the latent structural relationships between the dimensions of the PRO scale. The SEM results showed complex causal relationships between symptom clusters, with the most significant interactions between social function and psychological/emotional symptoms, dyspepsia and reflux, and reflux and bowel irregularity. Previous studies have found that patients with anxiety and depression have a high rate of social function impairment (50%-70%) and significant correlations with gastrointestinal dysfunction (Rus Makovec et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The relationship between dyspepsia and reflux is driven by gastrointestinal motility disorders, primarily manifested as delayed gastric emptying, gastric fundus relaxation disorder, or high-pressure conditions at the gastroesophageal junction (Vavricka et al. 2019). Previous studies have found that over 30% of patients with functional dyspepsia (FD) also have gastroesophageal reflux disease (GERD), with a high comorbidity rate (Li et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The causal link between reflux and bowel irregularity may be related to overall gastrointestinal dysfunction and psychological factors. GERD may affect gastrointestinal motility, leading to bowel issues, and vice versa (Lee et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, anxiety and depression often coexist in these diseases, potentially exacerbating symptoms and creating a vicious cycle (e.g., anxiety worsens gastrointestinal symptoms, which in turn worsens anxiety). The SEM results not only align with previous research but also demonstrate the close causal relationship of this phenomenon.\u003c/p\u003e \u003cp\u003eThe ANN analysis showed that the scores on the Self-Rating Anxiety Scale (SAS) and Self-Rating Depression Scale (SDS) have high accuracy in predicting patients' total PRO scores. Anxiety and depression are two important psychological factors that first significantly affect general symptoms, followed by other symptom clusters (e.g., gastrointestinal symptoms, psychological/emotional symptoms, and social function). The possible reason for this is that anxiety and depression are often accompanied by abnormal neuroendocrine responses. Anxiety and depression activate the hypothalamic-pituitary-adrenal (HPA) axis, leading to increased secretion of cortisol (Ball et al., 2022). Psychological stress may exacerbate functional gastrointestinal symptoms (e.g., dyspepsia, bowel irregularities) through neuroendocrine pathways such as HPA axis activation, rather than directly driving organic inflammation (Vavricka et al., 2019). Long-term elevated cortisol levels not only suppress immune function but may also contribute to systemic symptoms such as fatigue and muscle aches (Salimiyan et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Anxiety and depression can also stimulate the sympathetic nervous system (the \"fight or flight\" response), leading to the release of adrenaline and noradrenaline, which in turn triggers general symptoms (Speers et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Studies have also shown that patients with depression often exhibit elevated inflammatory markers such as C-reactive protein (CRP) and cytokines (e.g., IL-6, TNF-α) in serum (Osimo et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These inflammatory factors may affect various systems in the body, leading to the aggravation of general symptoms. Animal studies have further demonstrated that chronic stress induces behavioral abnormalities through immune dysregulation (Gu et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, long-term psychological stress may result in the loss of immune tolerance, leaving the body in a hypersensitive state that is more susceptible to environmental factors (Morris et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), further exacerbating general symptoms. On this basis, anxiety and depression, along with general symptoms, can regulate gut motility and secretion via the gut-brain axis, leading to gastrointestinal discomfort symptoms such as nausea, stomach pain, bloating, and diarrhea (Hillestad et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Other studies have also shown that anxiety and depression symptoms may affect gut function by altering the gut microbiota composition, which in turn causes symptoms (Simpson et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The mechanism may involve a decrease in beneficial gut bacteria and an increase in harmful bacteria, affecting normal gastrointestinal function and further exacerbating gastrointestinal symptoms. Ultimately, long-term symptoms destabilize patients' emotional states, leading to emotional dysregulation and social dysfunction. This causal chain reflects the complex interactions between symptoms in chronic gastrointestinal disease patients. Psychological factors have significant direct and indirect effects on symptoms such as dyspepsia and bowel irregularity, which is consistent with previous studies on the impact of mental health on chronic gastrointestinal diseases, providing theoretical support for future intervention strategies. This non-linear relationship complements SEM's ability to handle complex data relationships and further strengthens the understanding of the impact of psychological states on patient-reported outcomes.\u003c/p\u003e"},{"header":"4. Conclusion and Future Directions","content":"\u003cp\u003eThis study is the first to comprehensively utilize LCA, SEM, and ANN methods to explore the significant role of anxiety and depression (SAS and SDS scores) in exacerbating various gastrointestinal symptoms, particularly general and digestive symptoms. The results reveal the heterogeneity and complexity of symptoms in patients with chronic gastrointestinal diseases. If clinical treatment focuses solely on gastrointestinal symptoms related to the primary diagnosis, while neglecting the interactions between symptom clusters, achieving comprehensive symptom relief and improving patients' quality of life may be difficult. Therefore, clinical treatment plans should be more personalized, considering the interactions between symptom clusters and adopting a multidimensional approach to enhance effectiveness.\u003c/p\u003e \u003cp\u003eMoreover, the study highlights the importance of early intervention in patients' mental health issues, such as anxiety and depression. This not only helps improve overall symptoms but also alleviates gastrointestinal symptoms and enhances social functioning. By integrating the management of psychological factors, systemic symptoms, and gastrointestinal symptoms, more personalized and effective treatment plans can be developed, ultimately improving patients' quality of life. The heterogeneity of the study sample (including both functional and organic diseases) may have influenced the latent classification results. Future studies should also validate these findings through subgroup analyses in more homogeneous populations. Future research should expand sample sizes and explore the interactions among additional psychological, social, and physiological factors to enhance the generalizability and reliability of the results. It is important to note that our findings are based on a sample within the Chinese educational system. Differences in educational structures across countries (e.g., vocational training pressures) may limit the generalizability of the conclusions, and cross-cultural studies are needed to validate these findings. Additionally, further studies combining biomarkers can refine predictive models and deepen the understanding of the causal relationships between anxiety, depression, and systemic symptoms.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Key Research Project of Anhui Provincial Higher Education Institutions (2022AH050490); the Special Fund Project of the Academic Capacity Improvement Program for Integrative Chinese and Western Medicine Nursing (ZXYJHHL-K-2023-M14); the Doctoral Research Start-up Fund of Anhui University of Chinese Medicine (2025rcyb18); and the 2025 Program for the Training of Young and Middle-aged Teachers in Higher Education Institutions (JWFX2025020). The funding bodies had no role in the study design, data collection, analysis, interpretation of data, or writing of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Medical Ethics Committee of the First Affiliated Hospital of Anhui University of Chinese Medicine (Approval No. 2022MCZQ04). All procedures involving human participants were conducted in accordance with the Declaration of Helsinki and relevant institutional guidelines. Written informed consent was obtained from all participants prior to enrollment.\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\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRW (Wang Rui) designed the study, performed statistical analyses (LCA/SEM/ANN), and drafted the manuscript. CY (Yang Caiyi), BC (Sun Baocheng), and WH (Zhang Wenhui) collected the data and assisted with data entry. SM (Chen Shimin), YX (Niu Yingxue), and M (Hu Min) contributed to data cleaning and interpretation of the results. YF (Li Yifang) supervised the study, provided critical revisions, and acted as corresponding author. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors sincerely thank all participants and staff involved in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI and AI-assisted technologies in the writing process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this manuscript the authors used ChatGPT (OpenAI) for language editing to improve readability. The authors reviewed and edited the content as necessary and take full responsibility for the content of the publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBall, J., \u0026amp; Darby, I. (2022). Mental health and periodontal and peri-implant diseases. \u003cem\u003ePeriodontology 2000\u003c/em\u003e, 90(1), 106\u0026ndash;124. https://doi.org/10.1111/prd.12452\u003c/li\u003e\n\u003cli\u003eBisgaard, T. H., Allin, K. H., Keefer, L., Ananthakrishnan, A. N., \u0026amp; Jess, T. (2022). Depression and anxiety in inflammatory bowel disease: epidemiology, mechanisms and treatment. Nature reviews. \u003cem\u003eGastroenterology \u0026amp; hepatology\u003c/em\u003e, 19(11), 717\u0026ndash;726. https://doi.org/10.1038/s41575-022-00634-6\u003c/li\u003e\n\u003cli\u003eChinese Society of Gastroenterology, Cancer Collaboration Group of Chinese Society of Gastroenterology, Chinese Medical Association (2023). Guidelines for diagnosis and treatment of chronic gastritis in China (2022, Shanghai). \u003cem\u003eJournal of digestive diseases\u003c/em\u003e, 24(3), 150\u0026ndash;180. https://doi.org/10.1111/1751-2980.13193\u003c/li\u003e\n\u003cli\u003eDahmer, M. K., Yang, G., Zhang, M., Quasney, M. W., Sapru, A., Weeks, H. M., Sinha, P., Curley, M. A. Q., Delucchi, K. L., Calfee, C. S., Flori, H., RESTORE and BALI study investigators, \u0026amp; Pediatric Acute Lung Injury and Sepsis Investigators (PALISI) Network (2022). Identification of phenotypes in paediatric patients with acute respiratory distress syndrome: a latent class analysis. \u003cem\u003eThe Lancet. Respiratory medicine\u003c/em\u003e, 10(3), 289\u0026ndash;297. https://doi.org/10.1016/S2213-2600(21)00382-9\u003c/li\u003e\n\u003cli\u003eFu, X., Yan, T., Tian, Y., Niu, X., Xu, X., Wei, Y., Hu, Q., Ouyang, Z., \u0026amp; Wu, X. (2022). Exploring Factors Influencing Students\u0026apos; Entrepreneurial Intention in Vocational Colleges Based on Structural Equation Modeling: Evidence From China. \u003cem\u003eFrontiers in psychology\u003c/em\u003e, 13, 898319. https://doi.org/10.3389/fpsyg.2022.898319\u003c/li\u003e\n\u003cli\u003eGu, Y., Ye, T., Tan, P., Tong, L., Ji, J., Gu, Y., Shen, Z., Shen, X., Lu, X., \u0026amp; Huang, C. (2021). Tolerance-inducing effect and properties of innate immune stimulation on chronic stress-induced behavioral abnormalities in mice.\u003cem\u003e Brain, behavior, and immunity\u003c/em\u003e, 91, 451\u0026ndash;471. https://doi.org/10.1016/j.bbi.2020.11.002\u003c/li\u003e\n\u003cli\u003eHillestad, E. M. R., van der Meeren, A., Nagaraja, B. H., Bj\u0026oslash;rsvik, B. R., Haleem, N., Benitez-Paez, A., Sanz, Y., Hausken, T., Lied, G. A., Lundervold, A., \u0026amp; Berentsen, B. (2022). Gut bless you: The microbiota-gut-brain axis in irritable bowel syndrome. \u003cem\u003eWorld journal of gastroenterology\u003c/em\u003e, 28(4), 412\u0026ndash;431. https://doi.org/10.3748/wjg.v28.i4.412\u003c/li\u003e\n\u003cli\u003eHong, Q. Q., Yan, S., Zhao, Y. L., Fan, L., Yang, L., Zhang, W. B., Liu, H., Lin, H. X., Zhang, J., Ye, Z. J., Shen, X., Cai, L. S., Zhang, G. W., Zhu, J. M., Ji, G., Chen, J. P., Wang, W., Li, Z. R., Zhu, J. T., Li, G. X., \u0026hellip; You, J. (2024). Machine learning identifies the risk of complications after laparoscopic radical gastrectomy for gastric cancer. \u003cem\u003eWorld journal of gastroenterology\u003c/em\u003e, 30(1), 79\u0026ndash;90. https://doi.org/10.3748/wjg.v30.i1.79\u003c/li\u003e\n\u003cli\u003eIhara, E., Manabe, N., Ohkubo, H., Ogasawara, N., Ogino, H., Kakimoto, K., Kanazawa, M., Kawahara, H., Kusano, C., Kuribayashi, S., Sawada, A., Takagi, T., Takano, S., Tomita, T., Noake, T., Hojo, M., Hokari, R., Masaoka, T., Machida, T., Misawa, N., \u0026hellip; Higuchi, K. (2024). Evidence-Based Clinical Guidelines for Chronic Diarrhea 2023. \u003cem\u003eDigestion\u003c/em\u003e, 105(6), 480\u0026ndash;497. https://doi.org/10.1159/000541121\u003c/li\u003e\n\u003cli\u003eIwakiri, K., Fujiwara, Y., Manabe, N., Ihara, E., Kuribayashi, S., Akiyama, J., Kondo, T., Yamashita, H., Ishimura, N., Kitasako, Y., Iijima, K., Koike, T., Omura, N., Nomura, T., Kawamura, O., Ohara, S., Ozawa, S., Kinoshita, Y., Mochida, S., Enomoto, N., \u0026hellip; Koike, K. (2022). Evidence-based clinical practice guidelines for gastroesophageal reflux disease 2021. \u003cem\u003eJournal of gastroenterology\u003c/em\u003e, 57(4), 267\u0026ndash;285. https://doi.org/10.1007/s00535-022-01861-z\u003c/li\u003e\n\u003cli\u003eKhan, S., Sebastian, S. A., Parmar, M. P., Ghadge, N., Padda, I., Keshta, A. S., Minhaz, N., \u0026amp; Patel, A. (2024). Factors influencing the quality of life in inflammatory bowel disease: A comprehensive review. \u003cem\u003eDisease-a-month : DM\u003c/em\u003e, 70(1S), 101672. https://doi.org/10.1016/j.disamonth.2023.101672\u003c/li\u003e\n\u003cli\u003eKamada, T., Satoh, K., Itoh, T., Ito, M., Iwamoto, J., Okimoto, T., Kanno, T., Sugimoto, M., Chiba, T., Nomura, S., Mieda, M., Hiraishi, H., Yoshino, J., Takagi, A., Watanabe, S., \u0026amp; Koike, K. (2021). Evidence-based clinical practice guidelines for peptic ulcer disease 2020. \u003cem\u003eJournal of gastroenterology\u003c/em\u003e, 56(4), 303\u0026ndash;322. https://doi.org/10.1007/s00535-021-01769-0\u003c/li\u003e\n\u003cli\u003eLi, J. D., Wu, J., Zhao, Y., Wang, X., Jiang, F., Hou, Q., Chen, D. N., Zheng, R., Yu, R., Zhou, W., \u0026amp; Men, M. (2020). Phenotypic Spectrum of Idiopathic Hypogonadotropic Hypogonadism Patients With CHD7 Variants From a Large Chinese Cohort.\u003cem\u003e The Journal of clinical endocrinology and metabolism\u003c/em\u003e, 105(5), dgz182. https://doi.org/10.1210/clinem/dgz182\u003c/li\u003e\n\u003cli\u003eLi, X., \u0026amp; Jacobucci, R. (2022). Regularized structural equation modeling with stability selection. \u003cem\u003ePsychological methods\u003c/em\u003e, 27(4), 497\u0026ndash;518. https://doi.org/10.1037/met0000389\u003c/li\u003e\n\u003cli\u003eSinha, P., Calfee, C. S., \u0026amp; Delucchi, K. L. (2021). Practitioner\u0026apos;s Guide to Latent Class Analysis: Methodological Considerations and Common Pitfalls. \u003cem\u003eCritical care medicine\u003c/em\u003e, 49(1), e63\u0026ndash;e79. https://doi.org/10.1097/CCM.0000000000004710\u003c/li\u003e\n\u003cli\u003eLian, X., Lin, Y., Peng, X., Wang, Y., He, T., He, Z., Gu, W., Wang, H., He, F., \u0026amp; Huang, Y. (2023). Causal links between socioeconomic status, leisure sedentary behaviours and gastro-oesophageal reflux disease: a multivariable two-sample Mendelian randomisation study. \u003cem\u003eJournal of epidemiology and community health,\u003c/em\u003e 77(7), 460\u0026ndash;467. https://doi.org/10.1136/jech-2023-220311\u003c/li\u003e\n\u003cli\u003eLee, A., Kim, H. K., \u0026amp; Kim, H. (2024). High Prevalence and Risk Factors of Functional Gastrointestinal Disorders Among University Students in South Korea. \u003cem\u003eGastroenterology nursing : the official journal of the Society of Gastroenterology Nurses and Associates\u003c/em\u003e, 47(3), 195\u0026ndash;202. https://doi.org/10.1097/SGA.0000000000000798\u003c/li\u003e\n\u003cli\u003eMiwa, H., Nagahara, A., Asakawa, A., Arai, M., Oshima, T., Kasugai, K., Kamada, K., Suzuki, H., Tanaka, F., Tominaga, K., Futagami, S., Hojo, M., Mihara, H., Higuchi, K., Kusano, M., Arisawa, T., Kato, M., Joh, T., Mochida, S., Enomoto, N., \u0026hellip; Koike, K. (2022). Evidence-based clinical practice guidelines for functional dyspepsia 2021. \u003cem\u003eJournal of gastroenterology\u003c/em\u003e, 57(2), 47\u0026ndash;61. https://doi.org/10.1007/s00535-021-01843-7\u003c/li\u003e\n\u003cli\u003eMorris, G., Gevezova, M., Sarafian, V., \u0026amp; Maes, M. (2022). Redox regulation of the immune response. \u003cem\u003eCellular \u0026amp; molecular immunology\u003c/em\u003e, 19(10), 1079\u0026ndash;1101. https://doi.org/10.1038/s41423-022-00902-0\u003c/li\u003e\n\u003cli\u003eNagy-Szakal, D., Williams, B. L., Mishra, N., Che, X., Lee, B., Bateman, L., Klimas, N. G., Komaroff, A. L., Levine, S., Montoya, J. G., Peterson, D. L., Ramanan, D., Jain, K., Eddy, M. L., Hornig, M., \u0026amp; Lipkin, W. I. (2017). Fecal metagenomic profiles in subgroups of patients with myalgic encephalomyelitis/chronic fatigue syndrome. \u003cem\u003eMicrobiome\u003c/em\u003e, 5(1), 44. https://doi.org/10.1186/s40168-017-0261-y\u003c/li\u003e\n\u003cli\u003eOsimo, E. F., Pillinger, T., Rodriguez, I. M., Khandaker, G. M., Pariante, C. M., \u0026amp; Howes, O. D. (2020). Inflammatory markers in depression: A meta-analysis of mean differences and variability in 5,166 patients and 5,083 controls.\u003cem\u003e Brain, behavior, and immunity\u003c/em\u003e, 87, 901\u0026ndash;909. https://doi.org/10.1016/j.bbi.2020.02.010\u003c/li\u003e\n\u003cli\u003eRubin, D. T., Ananthakrishnan, A. N., Siegel, C. A., Sauer, B. G., \u0026amp; Long, M. D. (2019). ACG Clinical Guideline: Ulcerative Colitis in Adults. The American journal of gastroenterology, 114(3), 384\u0026ndash;413. https://doi.org/10.14309/ajg.0000000000000152\u003c/li\u003e\n\u003cli\u003eRus Makovec, M., Vintar, N., \u0026amp; Makovec, S. (2021). Level of Depression, Anxiety and Impairment of Social Relations with Regard to Pain Intensity in a Naturalistic Sample of Patients at the Outpatient Chronic Pain Clinic.\u003cem\u003e Psychiatria Danubina\u003c/em\u003e, 33(Suppl 4), 558\u0026ndash;564.\u003c/li\u003e\n\u003cli\u003eSalimiyan, S., Mohammadi, M., Aliakbari, S., Kazemi, R., Amini, A. A., \u0026amp; Rahmani, M. R. (2022). Hydrocortisone Long-term Treatment Effect on Immunomodulatory Properties of Human Adipose-Derived Mesenchymal Stromal/Stem Cells. \u003cem\u003eJournal of interferon \u0026amp; cytokine research : the official journal of the International Society for Interferon and Cytokine Research\u003c/em\u003e, 42(2), 72\u0026ndash;81. https://doi.org/10.1089/jir.2021.0120\u003c/li\u003e\n\u003cli\u003eSimpson, C. A., Diaz-Arteche, C., Eliby, D., Schwartz, O. S., Simmons, J. G., \u0026amp; Cowan, C. S. M. (2021). The gut microbiota in anxiety and depression - A systematic review.\u003cem\u003e Clinical psychology review\u003c/em\u003e, 83, 101943. https://doi.org/10.1016/j.cpr.2020.101943\u003c/li\u003e\n\u003cli\u003eSpeers, A. B., Cabey, K. A., Soumyanath, A., \u0026amp; Wright, K. M. (2021). Effects of Withania somnifera (Ashwagandha) on Stress and the Stress- Related Neuropsychiatric Disorders Anxiety, Depression, and Insomnia. \u003cem\u003eCurrent neuropharmacology\u003c/em\u003e, 19(9), 1468\u0026ndash;1495. https://doi.org/10.2174/1570159X19666210712151556\u003c/li\u003e\n\u003cli\u003eTanaka, S., Saitoh, Y., Matsuda, T., Igarashi, M., Matsumoto, T., Iwao, Y., Suzuki, Y., Nozaki, R., Sugai, T., Oka, S., Itabashi, M., Sugihara, K. I., Tsuruta, O., Hirata, I., Nishida, H., Miwa, H., Enomoto, N., Shimosegawa, T., \u0026amp; Koike, K. (2021). Evidence-based clinical practice guidelines for management of colorectal polyps.\u003cem\u003e Journal of gastroenterology\u003c/em\u003e, 56(4), 323\u0026ndash;335. https://doi.org/10.1007/s00535-021-01776-1\u003c/li\u003e\n\u003cli\u003eTang, X., Wang, P., Zhao, Y., et al. (2018). Experts questionnaires survey on the applicability for FGIDs of patient reported outcomes instrument for chronic gastrointestinal diseases. \u003cem\u003eChinese Journal of Integrative Medicine\u003c/em\u003e, 38(2), 182-185.\u003c/li\u003e\n\u003cli\u003eTiksnadi, B. B., Triani, N., Fihaya, F. Y., Turu\u0026apos; Allo, I. J., Iskandar, S., \u0026amp; Putri, D. A. E. (2023). Validation of Hospital Anxiety and Depression Scale in an Indonesian population: a scale adaptation study.\u003cem\u003e Family medicine and community health\u003c/em\u003e, 11(2), e001775. https://doi.org/10.1136/fmch-2022-001775\u003c/li\u003e\n\u003cli\u003eVavricka, S. R., \u0026amp; Greuter, T. (2019). Gastroparesis and Dumping Syndrome: Current Concepts and Management. \u003cem\u003eJournal of clinical medicine\u003c/em\u003e, 8(8), 1127. https://doi.org/10.3390/jcm8081127\u003c/li\u003e\n\u003cli\u003eWarren, A., Nyavor, Y., Beguelin, A., \u0026amp; Frame, L. A. (2024). Dangers of the chronic stress response in the context of the microbiota-gut-immune-brain axis and mental health: a narrative review.\u003cem\u003e Frontiers in immunology\u003c/em\u003e, 15, 1365871. https://doi.org/10.3389/fimmu.2024.1365871\u003c/li\u003e\n\u003cli\u003eZhang, J., Yu, P., Xu, Y., Lu, X. Y., Xu, Y., Hang, J., \u0026amp; Zhang, Y. (2024). Efficacy and Safety of a Low-FODMAP Diet in Combination with a Gluten-Free Diet for Adult Irritable Bowel Syndrome: A Systematic Review and Meta-Analysis. \u003cem\u003eDigestive diseases and sciences\u003c/em\u003e, 69(11), 4124\u0026ndash;4132. https://doi.org/10.1007/s10620-024-08671-8\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDistribution of Symptom Cluster Scores and Total PRO Scores\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003egeneral symptoms\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003edyspepsia\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ereflux\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eabnormal bowel movements\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003epsychological factors\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003esocial factors\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal PRO Score\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCategory 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.37\u0026thinsp;\u0026plusmn;\u0026thinsp;3.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.63\u0026thinsp;\u0026plusmn;\u0026thinsp;4.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.27\u0026thinsp;\u0026plusmn;\u0026thinsp;3.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.62\u0026thinsp;\u0026plusmn;\u0026thinsp;3.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.59\u0026thinsp;\u0026plusmn;\u0026thinsp;1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.47\u0026thinsp;\u0026plusmn;\u0026thinsp;9.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCategory2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.17\u0026thinsp;\u0026plusmn;\u0026thinsp;3.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.93\u0026thinsp;\u0026plusmn;\u0026thinsp;5.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.66\u0026thinsp;\u0026plusmn;\u0026thinsp;5.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.62\u0026thinsp;\u0026plusmn;\u0026thinsp;5.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.28\u0026thinsp;\u0026plusmn;\u0026thinsp;2.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.05\u0026thinsp;\u0026plusmn;\u0026thinsp;2.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.77\u0026thinsp;\u0026plusmn;\u0026thinsp;12.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCategory3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.30\u0026thinsp;\u0026plusmn;\u0026thinsp;6.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.31\u0026thinsp;\u0026plusmn;\u0026thinsp;5.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.46\u0026thinsp;\u0026plusmn;\u0026thinsp;6.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.31\u0026thinsp;\u0026plusmn;\u0026thinsp;1.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.84\u0026thinsp;\u0026plusmn;\u0026thinsp;2.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e78.52\u0026thinsp;\u0026plusmn;\u0026thinsp;16.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResults of Latent Classification\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eaBIC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEntropy\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup size (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3559.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3603.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3603.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3214.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3316.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3306.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4655/0.5345\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3177.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3306.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3316.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3379/0.2828/0.3793\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3175.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3362.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3363.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2793/0.2276/0.331/0.1621\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3174.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3409.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3409.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0586/0.3724/0.2276/0.2621/0.0793\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGeneral Demographic Characteristics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategory 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategory 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategory 3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026chi;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.228\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u0026ndash;45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.546\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u0026ndash;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.587\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnmarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.778\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStatus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducational\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBelow High School\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLevel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh School\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePostgraduate or Higher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTreatment History\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.417\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonthly Income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;3000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.619\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(࿥)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3000\u0026ndash;5000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5000\u0026ndash;8000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;8000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOccupation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStudent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.677\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFarmer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClerk / Staff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRetired\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDuration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;1 Year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026ndash;5 Years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;5 Years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ctable id=\"Tab4\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResults of Path Analysis Using Structural Equation Modeling\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePath\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStd.Err\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eZ-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStd.lv\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStd.all\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLatent Class~\u003c/p\u003e\n \u003cp\u003egeneral symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.696\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edyspepsia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.705\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ereflux\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.594\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eabnormal bowel movements\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.663\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epsychological factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.643\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esocial factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.645\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003egeneral symptoms~\u003c/p\u003e\n \u003cp\u003edyspepsia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.647\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ereflux\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.538\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eabnormal bowel movements\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.520\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epsychological factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.533\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esocial factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.511\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edyspepsia ~\u003c/p\u003e\n \u003cp\u003ereflux\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.832\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.400\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eabnormal bowel movements\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.215\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epsychological factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.662\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esocial factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ereflux ~\u003c/p\u003e\n \u003cp\u003eabnormal bowel movements\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.456\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epsychological factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.394\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esocial factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.409\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eabnormal bowel movements ~\u003c/p\u003e\n \u003cp\u003epsychological factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.466\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esocial factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.540\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epsychological factors ~\u003c/p\u003e\n \u003cp\u003esocial factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.585\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eClassification Evaluation Report of ANN\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePrecision\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRecall\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF1-Score\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSupport\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCategory 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCategory 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCategory 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccuracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMacro avg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted avg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Chronic gastrointestinal diseases, Latent Class Analysis, Structural Equation Modeling, Artificial Neural Networks, Psychological status","lastPublishedDoi":"10.21203/rs.3.rs-8357816/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8357816/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo explore the latent classification of patients with chronic gastrointestinal diseases using the Patient reported outcomes instrument for chronic gastrointestinal diseases (PRO) scale, and analyze the correlations between psychological status and quality of life.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003ePRO scale, SAS/SDS scores were collected from chronic gastrointestinal disease patients. Latent Class Analysis (LCA) was used to determine the optimal number of latent classes. Structural Equation Modeling (SEM) assessed correlations between symptom groups, and Artificial Neural Networks (ANN) analyzed the relationship between anxiety, depression, and symptom clusters.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 290 valid questionnaires were collected. Based on LCA, PRO scores were divided into three latent classes: 33.79%, 37.93%, and 28.28%. Univariate analysis showed that education level significantly affected classification (p\u0026thinsp;=\u0026thinsp;0.029). SEM analysis showed that all six symptom clusters significantly impacted latent classes, with dyspepsia (regression coefficient\u0026thinsp;=\u0026thinsp;0.71) having the most significant effect; Strong positive correlations were observed between general symptoms and indigestion (r\u0026thinsp;=\u0026thinsp;0.647) and reflux (r\u0026thinsp;=\u0026thinsp;0.538). ANN showed that anxiety and depression influenced symptom clusters, especially dyspepsia.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study reveals the complex interplay between symptoms and psychological factors in chronic gastrointestinal diseases. The identification of distinct symptom groups emphasizes the need for personalized treatment strategies. Addressing psychological factors like anxiety and depression, alongside physical symptoms, can enhance the management and quality of life for patients.\u003c/p\u003e","manuscriptTitle":"Latent Classification and Psychological Associations with Quality of Life in Chronic Gastrointestinal Disease Patients: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-19 09:11:41","doi":"10.21203/rs.3.rs-8357816/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-01-14T09:52:04+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-17T06:42:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-15T00:53:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-15T00:53:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychology","date":"2025-12-14T11:59:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d4b63cb2-0fd0-445c-b15c-396b3c5c3f18","owner":[],"postedDate":"January 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-19T09:11:41+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-19 09:11:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8357816","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8357816","identity":"rs-8357816","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}