Although substance use 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 substance use 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 and tobacco intake 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 larger than 90 percentages UKB participants with incomplete phenomic data to improve efficiency. Extensive numerical studies demonstrate the superiority of our method over competing methods, in terms of smaller estimation bias and variability. Our analysis results reveal significant effects for both frequent alcohol drinking and tobacco smoking by accelerating brain ageing in 0.24 and 0.32 years, respectively.

翻译：尽管已知物质使用与衰老过程中的认知功能下降相关，但其对中枢神经系统的直接影响尚不明确。本研究旨在通过估计不同酒精和烟草使用情况下的平均潜在脑龄差(BAG)指数（即大脑年龄与实际年龄之差），探究物质使用对加速大脑老化的潜在影响。研究基于英国生物样本库(UKB)大规模队列，该队列包含反映综合生活方式特征的广泛表型数据。我们面临两大挑战：（1）大量表型变量作为潜在混杂因素；（2）仅有小比例参与者具有完整表型数据。为应对这些挑战，我们首先开发了一种新型集成学习框架，用于在存在大量混杂因素时对平均潜在结局进行稳健估计。继而构建数据整合步骤，利用超过90%的具有不完整表型数据的UKB参与者信息以提高估计效率。大量数值研究表明，我们的方法在估计偏倚和变异性方面均优于现有方法。分析结果显示，频繁饮酒和吸烟分别通过加速大脑老化0.24年和0.32年产生显著效应。