Saeedinia Elham, Poursharifi Hamid, Momeni Fereshte, Vahedi Mohsen, Sadeghi Amir, Abdi Mansour, Ghahremani Ramin
Psychosis Research Center, Department of Clinical Psychology, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran.
Department of Biostatistics and Epidemiology, Psychosis Research Center, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran.
Gastroenterol Hepatol Bed Bench. 2025;18(1):100-114. doi: 10.22037/ghfbb.v18i1.3082.
AIM: This study examined the associations between psychosocial factors, Irritable bowel syndrome (IBS) diagnosis, and quality of life (QOL) in both control and IBS groups. Additionally, we explored the potential influence of psychosocial factors on the onset of IBS and developed a machine-learning model for IBS prediction. BACKGROUND: IBS is a prevalent gastrointestinal disorder, with various factors predicting its severity and associated symptoms. METHODS: Through convenience sampling, a cross-sectional study recruited participants diagnosed with IBS (n=134) and healthy controls (n=150) from Arak Gastroenterology Clinics. Linear regression assessed the impact of psychosocial factors on IBS symptom severity and QOL. Logistic regression analyzed the association of these factors with IBS onset. Machine learning algorithms were used to predict IBS based on psychosocial features. Instruments include IBS-SSS, IBS-QOL, Toronto Alexithymia Scale (TAS-20), Visceral Sensitivity Index (VSI), and Pain Catastrophe Scale (PCS). RESULTS: A total of 284 participants (61.27% females) were recruited in the study, with a mean age of 36.48±10.75 years. Compared to controls, IBS patients exhibited significantly higher scores on measures of pain catastrophizing scale (PCS, 40.95 vs. 27.73), somatization (13.91 vs. 6.49), and alexithymia (60.23 vs. 54.71) as well as lower VSI (40.54 vs. 72.10). For those with IBS, only difficulty identifying feelings and somatization remained associated with worse symptoms, while VSI presented an inverse correlation. Psychological factors were inversely related to QOL. Elevated levels of alexithymia (OR 1.06; 95% CI 0.48, 1.63), somatization (OR 1.80; 95%CI 1.12, 2.48), and PCS (OR 1.70; 95% CI 1.30, 2.10) were associated with a higher chance of developing IBS, while higher VSI (OR -1.65; 95% CI -1.89, -1.42) was protective. Among machine learning models, logistic regression based on these factors (excluding alexithymia) and age achieved good performance (AUC: 0.86, 95% CI: 0.78-0.94; Accuracy: 0.83, 95% CI: 0.73-0.90) in predicting IBS onset. CONCLUSION: Psychological factors were linked to worse IBS symptoms and quality of life. A machine learning model for IBS prediction presented promising results.
目的：本研究探讨了心理社会因素、肠易激综合征（IBS）诊断与对照组和IBS组生活质量（QOL）之间的关联。此外，我们还探究了心理社会因素对IBS发病的潜在影响，并开发了一种用于IBS预测的机器学习模型。 背景：IBS是一种常见的胃肠道疾病，有多种因素可预测其严重程度及相关症状。 方法：通过便利抽样，一项横断面研究从阿拉克胃肠病诊所招募了被诊断为IBS的参与者（n = 134）和健康对照组（n = 150）。线性回归评估心理社会因素对IBS症状严重程度和生活质量的影响。逻辑回归分析这些因素与IBS发病的关联。使用机器学习算法基于心理社会特征预测IBS。所使用的工具包括IBS-SSS、IBS-QOL、多伦多述情障碍量表（TAS-20）、内脏敏感性指数（VSI）和疼痛灾难化量表（PCS）。 结果：本研究共招募了284名参与者（61.27%为女性），平均年龄为36.48±10.75岁。与对照组相比，IBS患者在疼痛灾难化量表（PCS，40.95对27.73）、躯体化（13.91对6.49）和述情障碍（60.23对54.71）方面得分显著更高，而VSI更低（40.54对72.10）。对于IBS患者，只有难以识别情感和躯体化与更严重的症状相关，而VSI呈负相关。心理因素与生活质量呈负相关。述情障碍水平升高（OR 1.06；95%CI 0.48，1.63）、躯体化（OR 1.80；95%CI 1.12，2.48）和PCS（OR 1.70；95%CI 1.30，2.10）与患IBS的可能性更高相关，而更高的VSI（OR -1.65；95%CI -1.89，-1.42）具有保护作用。在机器学习模型中，基于这些因素（不包括述情障碍）和年龄的逻辑回归在预测IBS发病方面表现良好（AUC：0.86，95%CI：0.78 - 0.94；准确率：0.83，95%CI：0.73 - 0.90）。 结论：心理因素与更严重的IBS症状和生活质量相关。一种用于IBS预测的机器学习模型呈现出有前景的结果。
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