Smoking contributes to a variety of neurodegenerative diseases and neurobiological abnormalities, suggesting that smoking is associated with accelerated brain aging. However, the neurobiological mechanisms affected by smoking, and whether they are genetically influenced, remain to be investigated.

Using structural magnetic resonance imaging data from the UK Biobank (n = 33 293), a brain age predictor was trained on non-smoking healthy groups and tested on smokers to obtain the BrainAge Gap (BAG). The cumulative effect of multiple common genetic variants associated with smoking was then calculated to acquire a polygenic risk score (PRS). The relationship between PRS, BAG, total gray matter volume (tGMV), and smoking parameters was explored and further genes included in the PRS were annotated to identify potential molecular mechanisms affected by smoking.

The BrainAge in smokers was predicted with very high accuracy (r = 0.725, MAE = 4.16). Smokers had a greater BAG (Cohen's d = 0.074, p < 0.0001) and higher PRS (Cohen's d = 0.63, p < 0.0001) than non-smokers. A higher PRS was associated with increased amount of smoking, mediated by BAG and tGMV. Several neurotransmitters and ion channel pathways were enriched in the group of smoking-related genes involved in addiction, brain synaptic plasticity, and some neurological disorders.

By using a simplified single indicator of the entire brain (BAG) in combination with the PRS, this study highlights the greater BAG in smokers and its linkage with genes and smoking behavior, providing insight into the neurobiological underpinnings and potential features of smoking-related aging.

Older brain age – as estimated from structural MRI data – is known to be associated with detrimental mental and physical health outcomes in older adults. Social isolation, which has similar detrimental effects on health, may be associated with accelerated brain aging though little is known about how different trajectories of social isolation across the life course moderate this association. We examined the associations between social isolation trajectories from age 5 to age 38 and brain age assessed at age 45.

We previously created a typology of social isolation based on onset during the life course and persistence into adulthood, using group-based trajectory analysis of longitudinal data from a New Zealand birth cohort. The typology comprises four groups: ‘never-isolated’, ‘adult-only’, ‘child-only’, and persistent ‘child-adult’ isolation. A brain age gap estimate (brainAGE) – the difference between predicted age from structural MRI date and chronological age – was derived at age 45. We undertook analyses of brainAGE with trajectory group as the predictor, adjusting for sex, family socio-economic status, and a range of familial and child-behavioral factors.

Older brain age in mid-adulthood was associated with trajectories of social isolation after adjustment for family and child confounders, particularly for the ‘adult-only’ group compared to the ‘never-isolated’ group.

Although our findings are associational, they indicate that preventing social isolation, particularly in mid-adulthood, may help to avert accelerated brain aging associated with negative health outcomes later in life.

No significant relationships.