Koldaş Seda Sevilay, Sezerman Osman Uğur, Timuçin Emel
Biostatistics and Bioinformatics, School of Health Science, Acıbadem Mehmet Ali Aydınlar University, , Istanbul, Turkey.
Biostatistics and Bioinformatics, School of Health Science, Acıbadem Mehmet Ali Aydınlar University, Istanbul, Turkey.
mSystems. 2025 Jun 17;10(6):e0019625. doi: 10.1128/msystems.00196-25. Epub 2025 May 28.
Human microbiome plays a crucial role in host health and disease by mediating the impact of environmental factors on clinical outcomes. Mediation analysis is a valuable tool for dissecting these complex relationships. However, existing approaches are primarily designed for cross-sectional studies. Modern clinical research increasingly utilizes long follow-up periods, leading to complex data structures, particularly in metagenomic studies. To address this limitation, we introduce a novel mediation framework based on structural equation modeling that leverages linear mixed-effects models using penalized quasi-likelihood estimation with a debiased lasso. We applied this framework to a 16S rRNA sputum microbiome data set collected from patients with cystic fibrosis over 10 years to investigate the mediating role of the microbiome in the relationship between clinical states, disease aggressiveness phenotypes, and lung function. We identified richness as a key mediator of lung function. Specifically, was found to be significantly associated with mediating the decline in lung function on treatment compared to exacerbation, while was associated with the decline in lung function on recovery. This approach offers a powerful new tool for understanding the complex interplay between microbiome and clinical outcomes in longitudinal studies, facilitating targeted microbiome-based interventions.
Understanding the mechanisms by which the microbiome influences clinical outcomes is paramount for realizing the full potential of microbiome-based medicine, including diagnostics and therapeutics. Identifying specific microbial mediators not only reveals potential targets for novel therapies and drug repurposing but also offers a more precise approach to patient stratification and personalized interventions. While traditional mediation analyses are ill-equipped to address the complexities of longitudinal metagenomic data, our framework directly addresses this gap, enabling robust investigation of these increasingly common study designs. By applying this framework to a decade-long cystic fibrosis study, we have begun to unravel the intricate relationships between the sputum microbiome and lung function decline across different clinical states, yielding insights that were previously unknown.
人类微生物群通过介导环境因素对临床结局的影响，在宿主健康和疾病中发挥着关键作用。中介分析是剖析这些复杂关系的宝贵工具。然而，现有方法主要是为横断面研究设计的。现代临床研究越来越多地采用长时间随访，导致数据结构复杂，尤其是在宏基因组研究中。为解决这一局限性，我们引入了一种基于结构方程模型的新型中介框架，该框架利用线性混合效应模型，采用惩罚拟似然估计和去偏套索。我们将此框架应用于从囊性纤维化患者中收集的长达10年的16S rRNA痰微生物群数据集，以研究微生物群在临床状态、疾病侵袭性表型和肺功能之间关系中的中介作用。我们确定丰富度是肺功能的关键中介因素。具体而言，发现[此处原文缺失具体内容]与介导治疗时相比病情加重时肺功能下降显著相关，而[此处原文缺失具体内容]与恢复时肺功能下降相关。这种方法为理解纵向研究中微生物群与临床结局之间的复杂相互作用提供了一个强大的新工具，有助于基于微生物群的靶向干预。
了解微生物群影响临床结局的机制对于实现基于微生物群的医学（包括诊断和治疗）的全部潜力至关重要。识别特定的微生物中介因素不仅揭示了新疗法和药物重新利用的潜在靶点，还提供了一种更精确的患者分层和个性化干预方法。虽然传统的中介分析无法应对纵向宏基因组数据的复杂性，但我们的框架直接解决了这一差距，能够对这些日益常见的研究设计进行有力调查。通过将此框架应用于长达十年的囊性纤维化研究，我们已开始揭示不同临床状态下痰微生物群与肺功能下降之间的复杂关系，得出了以前未知的见解。
