[论文解读] Log-Contrast Regression with Functional Compositional Predictors: Linking Preterm Infant's Gut Microbiome Trajectories in Early Postnatal Period to Neurobehavioral Outcome

Zhe Sun, Wanli Xu+2 more|arXiv (Cornell University)|Aug 7, 2018

Gut microbiota and health被引用 3

一句话总结

本文提出了一种具有功能组成预测变量的稀疏对数对比回归模型，用于将早产儿出生后第一个月内肠道菌群的纵向轨迹与后续神经行为结果相关联。通过保持功能单纯形结构并建模随时间变化的、稀疏的和累积的影响，该方法识别出关键的菌群标志物及其动态影响，揭示了早期生活压力可能通过脑-肠轴影响神经发育的机制。

ABSTRACT

The neonatal intensive care unit (NICU) experience is known to be one of the most crucial factors that drive preterm infant's neurodevelopmental and health outcomes. It is hypothesized that stressful early life experience of very preterm neonate is imprinting gut microbiome by the regulation of the so-called brain-gut axis, and consequently, certain microbiome markers are predictive of later infant neurodevelopment. To investigate, a preterm infant study was conducted; infant fecal samples were collected during the infants' first month of postnatal age, resulting in functional compositional microbiome data, and neurobehavioral outcomes were measured when infants reached 36-38 weeks of post-menstrual age. To identify potential microbiome markers and estimate how the trajectories of gut microbiome compositions during early postnatal stage impact later neurobehavioral outcomes of the preterm infants, we innovate a sparse log-contrast regression with functional compositional predictors. The functional simplex structure is strictly preserved, and the functional compositional predictors are allowed to have sparse, smoothly varying, and accumulating effects on the outcome through time. Through a pragmatic basis expansion step, the problem boils down to a linearly constrained sparse group regression, for which we develop an efficient algorithm and obtain theoretical performance guarantees. Our approach yields insightful results in the preterm infant study. The identified microbiome markers and the estimated time dynamics of their impact on the neurobehavioral outcome shed light on the linkage between stress accumulation in early postnatal stage and neurodevelopmental process of infants.

研究动机与目标

• 研究早产儿出生后早期肠道菌群发育与长期神经行为结果之间的关联。
• 建模肠道菌群组成对神经发育结果的随时间变化、稀疏且累积的影响。
• 在整个分析过程中保持组成性肠道菌群数据的功能单纯形结构。
• 为功能组成回归开发一种计算高效且具有理论性能保证的方法。

提出的方法

• 该方法采用专为单纯形流形上的功能组成预测变量设计的稀疏对数对比回归框架。
• 应用实用的基展开，将功能组成预测变量转换为适合稀疏组回归的形式。
• 该方法在时间上对系数函数施加稀疏性和平滑性，以捕捉对结果的累积影响。
• 问题被重新表述为带有线性约束的稀疏组回归，以实现高效优化。
• 开发了一种具有理论性能保证的高效算法，确保统计可靠性。
• 该方法在整个分析过程中严格遵守肠道菌群组成的函数单纯形结构。

实验结果

研究问题

• RQ1 哪些特定的肠道菌群类群可预测早产儿后期的神经行为结果？
• RQ2 肠道菌群组成对神经发育的影响在出生后第一个月内如何动态变化？
• RQ3 新生儿重症监护室（NICU）环境中累积的压力因素在多大程度上塑造了影响神经发育的菌群轨迹？
• RQ4 如何在保持单纯形约束的前提下，对具有随时间变化影响的功能组成数据进行建模？

主要发现

• 该模型成功识别出可预测早产儿后期神经行为结果的关键菌群标志物。
• 肠道菌群组成对神经发育的时变影响被发现具有稀疏性和累积性，表明存在关键的影响窗口。
• 该方法揭示，某些微生物类群对神经行为结果的影响随出生后时间推移而逐渐增强。
• 识别出的菌群标志物及其动态影响模式为早期神经发育中脑-肠轴机制提供了生物学见解。
• 该方法展现出强大的理论与计算性能，使对功能组成数据的可靠推断成为可能。
• 结果支持了早期NICU生活压力塑造菌群轨迹并进而影响长期神经发育结果的假设。