The Effect of Alcohol intake on Brain White Matter Microstructural Integrity: A New Causal Inference Framework for Incomplete Phenomic Data

Although substance use, such as alcohol intake, is known to be associated with cognitive decline during aging, its direct influence on the central nervous system remains incompletely understood. In this study, we investigate the influence of alcohol intake frequency on reduction of brain white matter microstructural integrity in the fornix, a brain region considered a promising marker of age-related microstructural degeneration, using a large UK Biobank (UKB) cohort with extensive phenomic data reflecting a comprehensive lifestyle profile. Two major challenges arise: 1) potentially nonlinear confounding effects from phenomic variables and 2) a limited proportion of participants with complete phenomic data. To address these challenges, we develop a novel ensemble learning framework tailored for robust causal inference and introduce a data integration step to incorporate information from UKB participants with incomplete phenomic data, improving estimation efficiency. Our analysis reveals that daily alcohol intake may significantly reduce fractional anisotropy, a neuroimaging-derived measure of white matter structural integrity, in the fornix and increase systolic and diastolic blood pressure levels. Moreover, extensive numerical studies demonstrate the superiority of our method over competing approaches in terms of estimation bias, while outcome regression-based estimators may be preferred when minimizing mean squared error is prioritized.

尽管已知酒精摄入等物质使用行为与衰老过程中的认知衰退存在关联，但其对中枢神经系统（central nervous system）的直接影响仍未完全阐明。本研究依托拥有全面生活方式特征表型组数据（phenomic data）的大型英国生物样本库（UK Biobank, UKB）队列，探究酒精摄入频率对穹窿（fornix）脑区白质微结构完整性降低的影响——该脑区被认为是年龄相关微结构退变的潜在标志物。本研究面临两大挑战：1）表型变量可能存在非线性混杂效应；2）拥有完整表型数据的参与者比例有限。为解决这些问题，我们开发了一种专为稳健因果推断（robust causal inference）设计的新型集成学习框架（ensemble learning framework），并引入数据整合步骤以纳入英国生物样本库中表型数据不完整的参与者信息，从而提升了估计效率。分析结果显示，每日饮酒可能显著降低穹窿脑区的各向异性分数（fractional anisotropy）——一种反映脑白质结构完整性的神经影像学衍生指标——并升高收缩压与舒张压水平。此外，大规模数值模拟研究表明，我们提出的方法在估计偏差方面优于同类竞争方法；而当优先考虑最小化均方误差（mean squared error, MSE）时，基于结果回归的估计量则更为适用。