Ji Jiadong, Yuan Zhongshang, Zhang Xiaoshuai, Li Fangyu, Xu Jing, Liu Ying, Li Hongkai, Wang Jia, Xue Fuzhong
Department of Epidemiology and Biostatistics, School of Public Health, Shandong University, Jinan, China.
Department of Neurology, Capital Medical University, Xuanwu Hospital, Beijing, China.
BMJ Open. 2015 Jan 16;5(1):e006721. doi: 10.1136/bmjopen-2014-006721.
Identification of pathway effects responsible for specific diseases has been one of the essential tasks in systems epidemiology. Despite some advance in procedures for distinguishing specific pathway (or network) topology between different disease status, statistical inference at a population level remains unsolved and further development is still needed. To identify the specific pathways contributing to diseases, we attempt to develop powerful statistics which can capture the complex relationship among risk factors.
Acute myeloid leukaemia (AML) data obtained from 133 adults (98 patients and 35 controls; 47% female).
Simulation studies indicated that the proposed Pathway Effect Measures (PEM) were stable; bootstrap-based methods outperformed the others, with bias-corrected bootstrap CI method having the highest power. Application to real data of AML successfully identified the specific pathway (Treg→TGFβ→Th17) effect contributing to AML with p values less than 0.05 under various methods and the bias-corrected bootstrap CI (-0.214 to -0.020). It demonstrated that Th17-Treg correlation balance was impaired in patients with AML, suggesting that Th17-Treg imbalance potentially plays a role in the pathogenesis of AML.
The proposed bootstrap-based PEM are valid and powerful for detecting the specific pathway effect contributing to disease, thus potentially providing new insight into the underlying mechanisms and ways to study the disease effects of specific pathways more comprehensively.
识别导致特定疾病的通路效应一直是系统流行病学的重要任务之一。尽管在区分不同疾病状态下的特定通路（或网络）拓扑结构的程序方面取得了一些进展，但在人群水平上的统计推断仍未解决，仍需进一步发展。为了识别导致疾病的特定通路，我们试图开发强大的统计方法，以捕捉风险因素之间的复杂关系。
从133名成年人（98例患者和35名对照；47%为女性）中获取急性髓系白血病（AML）数据。
模拟研究表明，所提出的通路效应测量方法（PEM）是稳定的；基于自助法的方法优于其他方法，偏差校正自助法置信区间（CI）方法的功效最高。将其应用于AML的实际数据，成功识别出导致AML的特定通路（调节性T细胞→转化生长因子β→辅助性T细胞17）效应，在各种方法下p值均小于0.05，偏差校正自助法CI为（-0.214至-0.020）。这表明AML患者中辅助性T细胞17-调节性T细胞的相关性平衡受损，提示辅助性T细胞17-调节性T细胞失衡可能在AML的发病机制中起作用。
所提出的基于自助法的PEM对于检测导致疾病的特定通路效应是有效且强大的，从而有可能为深入了解潜在机制提供新的见解，并为更全面地研究特定通路的疾病效应提供方法。
