Although substance use, such as alcohol consumption, is known to be associated with cognitive decline during ageing, its direct influence on the central nervous system remains unclear. In this study, we aim to investigate the potential influence of alcohol intake frequency on accelerated brain ageing by estimating the mean potential brain-age gap (BAG) index, the difference between brain age and actual age, under different alcohol intake frequencies in a large UK Biobank (UKB) cohort with extensive phenomic data reflecting a comprehensive life-style profile. We face two major challenges: (1) a large number of phenomic variables as potential confounders and (2) a small proportion of participants with complete phenomic data. To address these challenges, we first develop a new ensemble learning framework to establish robust estimation of mean potential outcome in the presence of many confounders. We then construct a data integration step to borrow information from UKB participants with incomplete phenomic data to improve efficiency. Our analysis results reveal that daily intake or even a few times a week may have significant effects on accelerating brain ageing. Moreover, extensive numerical studies demonstrate the superiority of our method over competing methods, in terms of smaller estimation bias and variability.

翻译：尽管已知酒精摄入等物质使用与衰老过程中的认知衰退相关，但其对中枢神经系统的直接影响仍不明确。本研究旨在通过估算不同饮酒频率下的潜在平均脑龄差距（BAG）指数（即大脑年龄与实际年龄的差值），探究饮酒频率对加速大脑老化的潜在影响。我们基于英国生物银行（UKB）大规模队列中反映全面生活方式特征的丰富表型数据开展分析，面临两大挑战：（1）大量表型变量作为潜在混杂因素；（2）仅少数参与者拥有完整表型数据。为解决上述问题，我们首先开发了一种新型集成学习框架，可在存在大量混杂因素时实现平均潜在结果的稳健估计；继而构建数据整合步骤，借鉴UKB中表型数据不完整参与者的信息以提升效率。分析结果显示，每日饮酒甚至每周数次饮酒均可能对加速大脑老化产生显著影响。此外，大量数值研究表明，本方法在估计偏差和变异性方面均优于现有方法。