  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.
