Decoding IBS: a machine learning approach to psychological distress and gut-brain interaction.

IF 2.5 3区 医学 Q2 GASTROENTEROLOGY & HEPATOLOGY BMC Gastroenterology Pub Date : 2024-08-15 DOI:10.1186/s12876-024-03355-z
Astri J Lundervold, Julie E Billing, Birgitte Berentsen, Gülen A Lied, Elisabeth K Steinsvik, Trygve Hausken, Arvid Lundervold
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Abstract

Purpose: Irritable bowel syndrome (IBS) is a diagnosis defined by gastrointestinal (GI) symptoms like abdominal pain and changes associated with defecation. The condition is classified as a disorder of the gut-brain interaction (DGBI), and patients with IBS commonly experience psychological distress. The present study focuses on this distress, defined from reports of fatigue, anxiety, depression, sleep disturbances, and performance on cognitive tests. The aim was to investigate the joint contribution of these features of psychological distress in predicting IBS versus healthy controls (HCs) and to disentangle clinically meaningful subgroups of IBS patients.

Methods: IBS patients ( n = 49 ) and HCs ( n = 28 ) completed the Chalder Fatigue Scale (CFQ), the Hamilton Anxiety and Depression Scale (HADS), and the Bergen Insomnia Scale (BIS), and performed tests of memory function and attention from the Repeatable Battery Assessing Neuropsychological Symptoms (RBANS). An initial exploratory data analysis was followed by supervised (Random Forest) and unsupervised (K-means) classification procedures.

Results: The explorative data analysis showed that the group of IBS patients obtained significantly more severe scores than HCs on all included measures, with the strongest pairwise correlation between fatigue and a quality measure of sleep disturbances. The supervised classification model correctly predicted belongings to the IBS group in 80% of the cases in a test set of unseen data. Two methods for calculating feature importance in the test set gave mental and physical fatigue and anxiety the strongest weights. An unsupervised procedure with K = 3 showed that one cluster contained 24% of the patients and all but two HCs. In the two other clusters, their IBS members were overall more impaired, with the following differences. One of the two clusters showed more severe cognitive problems and anxiety symptoms than the other, which experienced more severe problems related to the quality of sleep and fatigue. The three clusters were not different on a severity measure of IBS and age.

Conclusion: The results showed that psychological distress is an integral component of IBS symptomatology. The study should inspire future longitudinal studies to further dissect clinical patterns of IBS to improve the assessment and personalized treatment for this and other patient groups defined as disorders of the gut-brain interaction. The project is registered at https://classic.

Clinicaltrials: gov/ct2/show/NCT04296552 20/05/2019.

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解码肠易激综合征:心理困扰与肠脑互动的机器学习方法。
目的:肠易激综合征(IBS)是一种由胃肠道症状(如腹痛和排便相关变化)定义的诊断。肠易激综合征被归类为肠道-大脑相互作用(DGBI)紊乱,肠易激综合征患者通常会有心理困扰。本研究的重点是根据疲劳、焦虑、抑郁、睡眠障碍和认知测试表现的报告来界定这种困扰。目的是研究这些心理困扰特征在预测肠易激综合征与健康对照组(HCs)时的共同作用,并区分肠易激综合征患者中具有临床意义的亚组:肠易激综合征患者(49 人)和健康对照组(28 人)完成了查尔德疲劳量表(CFQ)、汉密尔顿焦虑抑郁量表(HADS)和卑尔根失眠量表(BIS),并进行了可重复神经心理症状评估电池(RBANS)中的记忆功能和注意力测试。初步探索性数据分析后,进行了有监督(随机森林)和无监督(K-means)分类程序:探索性数据分析结果表明,肠易激综合征患者在所有测量指标上的得分都明显高于高危人群,其中疲劳与睡眠障碍质量测量之间的配对相关性最强。在未见数据的测试集中,监督分类模型正确预测了 80% 的病例属于肠易激综合征组。在测试集中,有两种计算特征重要性的方法赋予精神和身体疲劳以及焦虑最强的权重。K = 3 的无监督程序显示,一个聚类包含了 24% 的患者和所有 HC,只有两个除外。在另外两个群组中,其 IBS 成员的总体受损程度较高,但存在以下差异。其中一个群组比另一个群组表现出更严重的认知问题和焦虑症状,而另一个群组则在睡眠质量和疲劳方面出现了更严重的问题。这三个群组在肠易激综合征的严重程度和年龄方面没有差异:结论:研究结果表明,心理困扰是肠易激综合征症状的一个组成部分。这项研究应能启发未来的纵向研究,进一步剖析肠易激综合征的临床模式,从而改进对这一群体以及其他被定义为肠道-大脑相互作用紊乱的患者群体的评估和个性化治疗。该项目注册于 https://classic.Clinicaltrials: gov/ct2/show/NCT04296552 20/05/2019。
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来源期刊
BMC Gastroenterology
BMC Gastroenterology 医学-胃肠肝病学
CiteScore
4.20
自引率
0.00%
发文量
465
审稿时长
6 months
期刊介绍: BMC Gastroenterology is an open access, peer-reviewed journal that considers articles on all aspects of the prevention, diagnosis and management of gastrointestinal and hepatobiliary disorders, as well as related molecular genetics, pathophysiology, and epidemiology.
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