Interactions between upper respiratory microbiota and psychological distress in pulmonary nodule patients: a retrospective nested case-control study based on machine learning and exploratory mediation analysis

医学 心理困扰 探索性分析 调解 呼吸窘迫 苦恼 上呼吸道感染 探索性研究 临床心理学 人工智能 机器学习 急性呼吸窘迫 重症监护医学 结核(地质) 呼吸系统 年轻人 梅德林 探索性因素分析 转录组 肺孤立结节 精神科 生物信息学
作者
Xin Ye,Shiyan Tan,Qiong Ma,Zifan Wang,Taoyu Wu,Z.Y. Mao,Qian Wang,Chuan Zheng,Xingchen Peng,Fengming You,Yifeng Ren
出处
期刊:International Journal of Surgery [Wolters Kluwer]
标识
DOI:10.1097/js9.0000000000004331
摘要

Background: The role of upper respiratory microbiota in the context of psychological distress (PD), particularly in patients with pulmonary nodules (PNs), remains largely unexplored. The study investigates anxiety and depression prevalence, clinical risk factors, and upper respiratory microbiota composition in patients with PNs, and their associations. Methods: A retrospective nested case-control study (August 2022 to May 2024) surveyed 381 PNs participants using a standardized questionnaire. Univariate analysis and logistic regression examined associations with PD. 16S ribosomal RNA sequencing compared microbiota between groups. Spearman correlation and exploratory mediation analysis explored associations between microbiota, clinical features, and PD. Machine learning algorithms were trained to identify features associated with PD. Results: This study analyzed the sociodemographic and disease characteristics of 381 participants with PNs, revealing that approximately 32% exhibited symptoms of PD. Results showed female patients (OR = 1.77), those with ground-glass nodules (OR = 1.77) and history of nodular disease (OR = 1.94) were associated with a higher prevalence of PD. The main differential bacterial genus between no psychological distress (NPD) and PD groups such as g_Prevotella, g_Neisseria, and g_Haemophilus . The machine learning algorithms revealed that factors such as g_Aggregatibacter, g_Rhodococcus and gender constituted the best predictive group (Area under the curve, AUC = 0.75). Furthermore, exploratory mediation analysis identified associations suggesting that g_Haemophilus (β = 0.738 and β = − 0.523), and g_Dialister (β = 0.452 and β = 0.29) may be involved in the links between BAI and BDI-II scores with clinical factors. Conclusion: Our findings highlight the complex associations among clinical factors, upper respiratory microbiota, and the presence of PD in patients with PNs, laying the groundwork for future research into whether microbiota modulation could be a strategy for improving mental health in PNs patients. Graphical Abstract
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