🤖 AI Summary
This study investigates the causal effects of alcohol consumption frequency on white matter microstructural integrity and accelerated brain aging, addressing methodological challenges posed by high-dimensional confounding and large-scale incomplete phenotypic data in the UK Biobank.
Method: We propose the first causal inference framework integrating ensemble learning with cross-individual information borrowing, enabling robust estimation of brain age gap (BAG) under missingness and complex confounding. The method adaptively regularizes high-dimensional covariates, substantially reducing both bias and variance.
Results: Empirical analysis reveals a dose-dependent association between moderate-to-heavy drinking (several times per week or daily) and significantly increased BAG—indicating accelerated brain aging. Our framework establishes a novel paradigm for causal neuroepidemiological inference under incomplete observational data, offering improved validity and generalizability for population-level brain health studies.
📝 Abstract
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.
