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分类依据与纠错

AI 根据所提供的论文材料归类:摘要明确提出处理非线性混杂和不完整资料的因果推断框架,并估计饮酒的健康效应。

反馈分类问题

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

饮酒对白质微结构完整性的影响:不完整表型数据的因果推断框架

正在核对关联版本…

代码、数据与补充材料

正在核对对应版本的资源…

关注这篇论文的作者
Chixiang Chen
Shuo Chen
Zhenyao Ye
Xu Shi
Tianzhou Ma
Michelle Shardell

饮酒对白质微结构完整性的影响:不完整表型数据的因果推断框架

研究问题
面对高维生活方式变量的潜在非线性混杂和大量不完整表型记录,如何估计饮酒频率对穹窿白质微结构完整性及相关健康指标的影响?
方法与思路
作者提出用于稳健因果推断的集成学习框架,并通过数据整合纳入表型资料不完整的 UK Biobank 参与者信息,以提高估计效率。
材料陈述的贡献
摘要称,每日饮酒可能降低穹窿的各向异性分数并升高血压;数值研究中该方法偏差较小,但以均方误差为目标时结局回归可能更优。
阅读关注点
应核对因果识别与缺失数据整合所需假设、饮酒暴露和目标效应的定义,并分别检查偏差、均方误差和区间覆盖率的比较依据。
阅读边界
依据 OpenAlex 索引摘要,稿件版本未注明。摘要未说明混杂控制和缺失机制的具体条件,观察数据分析不能仅凭摘要确认为因果事实。

原文摘要

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: (a) potentially nonlinear confounding effects from phenomic variables and (b) 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. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

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