Zhu Lingling, Xu Shuo, Guo Huaizhu, Lu Siqi, Gao Jiaqi, Hu Nan, Chen Chen, Liu Zuojing, Ji Xiaolin, Wang Kun, Duan Liping
Department of Gastroenterology, Peking University Third Hospital, Beijing 100191, China.
Beijing Key Laboratory for Helicobacter Pylori Infection and Upper Gastrointestinal Diseases, Beijing 100191, China.
J Transl Int Med. 2024 Oct 1;12(4):355-366. doi: 10.2478/jtim-2024-0009. eCollection 2024 Sep.
Symptom-based subtyping for functional gastrointestinal disorders (FGIDs) has limited value in identifying underlying mechanisms and guiding therapeutic strategies. Small intestinal dysbiosis is implicated in the development of FGIDs. We tested if machine learning (ML) algorithms utilizing both gastrointestinal (GI) symptom characteristics and lactulose breath tests could provide distinct clusters.
This was a prospective cohort study. We performed lactulose hydrogen methane breath tests and hydrogen sulfide breath tests in 508 patients with GI symptoms. An unsupervised ML algorithm was used to categorize subjects by integrating GI symptoms and breath gas characteristics. Generalized Estimating Equation (GEE) models were used to examine the longitudinal associations between cluster patterns and breath gas time profiles. An ML-based prediction model for identifying excessive gas production in FGIDs patients was developed and internal validation was performed.
FGIDs were confirmed in 300 patients. K-means clustering identified 4 distinct clusters. Cluster 2, 3, and 4 showed enrichments for abdominal distention and diarrhea with a high proportion of excessive gas production, whereas Cluster 1 was characterized by moderate lower abdominal discomforts with the most psychological complaints and the lowest proportion of excessive gas production. GEE models showed that breath gas concentrations varied among different clusters over time. We further sought to develop an ML-based prediction model to determine excessive gas production. The model exhibited good predictive capabilities.
ML-based phenogroups and prediction model approaches could provide distinct FGIDs subsets and efficiently determine FGIDs subsets with greater gas production, thereby facilitating clinical decision-making and guiding treatment.
基于症状的功能性胃肠病（FGIDs）亚型分类在识别潜在机制和指导治疗策略方面价值有限。小肠微生物群失调与FGIDs的发生有关。我们测试了利用胃肠道（GI）症状特征和乳果糖呼气试验的机器学习（ML）算法是否能提供不同的聚类。
这是一项前瞻性队列研究。我们对508例有胃肠道症状的患者进行了乳果糖氢甲烷呼气试验和硫化氢呼气试验。使用无监督ML算法，通过整合胃肠道症状和呼气气体特征对受试者进行分类。使用广义估计方程（GEE）模型检查聚类模式与呼气气体时间曲线之间的纵向关联。开发了一种基于ML的预测模型，用于识别FGIDs患者的气体过度产生情况，并进行了内部验证。
300例患者确诊为FGIDs。K均值聚类识别出4个不同的聚类。聚类2、3和4表现为腹胀和腹泻富集，气体过度产生比例高，而聚类1的特征是下腹部中度不适，心理抱怨最多，气体过度产生比例最低。GEE模型显示，不同聚类的呼气气体浓度随时间变化。我们进一步试图开发一种基于ML的预测模型来确定气体过度产生情况。该模型表现出良好的预测能力。
基于ML的表型组和预测模型方法可以提供不同的FGIDs亚组，并有效地确定气体产生较多的FGIDs亚组，从而促进临床决策和指导治疗。
